business value thesis

A Systems View Across Time and Space

• Open access
• Published: 16 January 2023

Disruptive business value models in the digital era

• Navitha Singh Sewpersadh   ORCID: orcid.org/0000-0002-3219-7974 1  

Journal of Innovation and Entrepreneurship volume  12 , Article number:  2 ( 2023 ) Cite this article

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The coronavirus pandemic illustrated how rapidly the global environment could be disrupted on many levels but also drive an acceleration in others. Business leaders are grappling with dysfunctional business models that are ill-equipped to manage the disruptive environment of growing artificial intelligence. Hence, this study examined the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An integrative review methodology was used in this study to develop theoretical constructs relating to business model innovation in the services sector. Key propositions were an innovation continuum, a responsive business innovation model and value architecture, which inculcates a sustainable value creation proposition and market advantage. Businesses must continuously evolve on the high end of the innovation continuum to reduce the risk of innovation apathy and strategic myopia. A key contribution of this study was the interdependencies in value networks that allow for collaborative working and co-creation of resources, such as crowdsourcing, crowdworking and social media platforms. This study also showed the growing importance of a centre of excellence to function at the forefront of disruptive technologies. A key finding was the need for governance structures to recognise and manage the trade-offs between value drivers, which sometimes may conflict with societal benefits. The integrative review revealed that customer relationship management, global business services and artificial intelligence had not been unified in the extant literature, which makes this paper novel in its contribution to businesses struggling with or opposed to the digital revolution.

Introduction

The evolution of technology has disrupted almost every business globally by continuously transforming, enhancing, and streamlining operational processes and procedures. Digitalisation Footnote 1 is disruptive and brings about discontinuous changes (Paiola & Gebauer, 2020 ), but it is a key element for new value-creation and revenue-generation opportunities for market competitiveness (Kamalaldin et al., 2020 ). Climate change, pandemics, environmental devastation and widening social inequalities have created an abrupt realisation that the existing business models are no longer ‘fit for purpose’. New practices, skills, operational processes, and business models are required to use artificial intelligence Footnote 2 (AI) to create value for customers (Sjödin et al., 2021 ). It is increasingly important for businesses to understand the evolving environment to assimilate for viability in the market and then innovate to gain a competitive advantage. Businesses face pressure to focus on achieving their non-financial goals and not just maximising profits (Rabaya & Saleh, 2022 ). The interconnected elements of environmental, societal and governance (ESG) have provided a catalyst to transform businesses to be more responsive toward the planet and people when pursuing profitability and growth. “The illiterate of the twenty-first century will not be those that cannot read or write, but those that cannot learn, unlearn and relearn” (Toffler, 1970 ). Refining, adapting, revising and reformulating a business model provides businesses with a roadmap for achieving holistic goals by harnessing the strategic advantages of AI technologies.

Digital transformations create new potential for organisations to redefine and optimise their operations by recognising the role of automation Footnote 3 in creating market differentiation and service excellence (Flyverbom et al., 2019 ; Zuboff, 1988 ). The COVID-19 pandemic affected critical business functions across organisations globally, thus serving as an accelerator of digital transformations and the reconfiguration of static business models. The pandemic affected how people operate and customer services are provided, particularly when governments imposed regulated lockdowns to protect human life. According to institutional theory, internal and external pressures (Zucker, 1987 ) accelerate the desire or compulsion to transform an organisation. One such pressure is disruptive digital technology, and the other is the pandemic. The traditional workforce has also been transformed into a blend of humans working collaboratively with AI.

A global survey conducted by Deloitte (2020) found that the largest concern for respondents during the pandemic was the viability of their business models. Some businesses led the business model innovation Footnote 4 , while other companies crumbled. As the contingency theory proposes (Lewin & Volberda, 1999 ), a suitable strategy is required to accomplish a strategic fit with an organisation’s market. Therefore, business model innovation is a key ingredient in underpinning a business resilience strategy, particularly with technological innovation rapidly changing the nature of work. These pressures to innovate in the digital era have widened the gap between innovators and stragglers in the business world. The advantages of conventional business processes that are human reliant are weakening, exposing the fragility of the human capital leverage model, which will be further impacted as AI evolves. Therefore, innovation laggards may fail should they not embrace the principle of accelerating disruptive technologies in their business models. As global economies face unprecedented disruption, a once disruptive business model can become static by becoming complacent or relying excessively on past strategies that may have become outdated. This risk of innovation apathy or myopia motivates businesses to have an agile business model that continually evolves with the disruptive digital era.

A business model is seen as a robust abstract instrument to model a framework for a company’s competitive stance (Hamel, 2000 ) by connecting technical potential with the recognition of economic value (Chesbrough, 2011 ). However, Teece ( 2010 ) argued that approaches to business models are diverse due to the absence of a theoretical grounding in economics or business studies. For this reason, there have been calls for research on business models and value propositions Footnote 5 focusing on market differentiation and industry disruption (Weinstein, 2020 ). Emerging market differentiators are concentrated on labour automation, such as Robotic Process Automation (RPA) and service bots used in Global Business Services (GBS) (OECD, 2007 ; SSON, 2018 ). However, codifiability and digitalisation in the global services literature are absent despite the advantages of the centrality of transaction costs and efficiencies (McWilliam et al., 2019 ). There is an ongoing call for researchers to adapt and extend how AI technologies can be aligned with business (Coltman et al., 2015 ; Santos et al., 2020 ; World Trade Organization, 2019 ). Moreover, a persistent gap exists in academic research regarding the business models using AI for digitalising Customer Relationship Management (CRM) in the global service sector. A necessary first step toward knowledge evolution and model building is a systematic exposition based on theory (Melville et al., 2004 ) and disruptive technology (Parmar et al., 2014 ) that drive an understanding of business model innovation (Teece, 2018 ) to capitalise on business opportunities that overcome pandemic challenges.

With digital servitisation Footnote 6 (Kohtamäki, et al., 2019 ; Vendrell-Herrero et al., 2017 ), the service sector is no longer operating as a separate category, since retailers and manufacturers are entering the service sector with smart services, such as Caterpillar, Michelin, Siemens and Voith Group. They transform their products by embedding software to communicate to the data cloud (Ng & Wakenshaw, 2017 ), which can then be analysed through advanced data analytics for co-created value-added services (Opresnik & Taisch, 2015 ). This study selected the service sector to examine business model innovation, since it is people-centred and an important contributor to the economic environment. A GBS structure was adopted in this study, because it allows the researcher flexibility to incorporate innovative systems with global mobility for the service sector’s offerings. The GBS business model also provides benefits of economies of scale, streamlined processes, superior service quality and scalability of operations through consolidating support functions into a single centre staffed with specialists. This article provides crucial theoretical framing by linking the CRM, GBS and service innovation technologies to business model innovation. This study contributes an innovation continuum, a responsive business innovation model and value propositions focused on market differentiation, service innovation and industry disruption. This study also provided a research agenda to catalyse future research.

Research methodology

This study employed a methodical means of assembling and synthesising previous research (Baumeister & Leary, 1997 ; Tranfield et al., 2003 ) through an integrative review process of experimental and non-experimental research with theoretical and empirical data (Whittemore & Knafl, 2005 ). This study adopted a concept-centric rather than a chronological or author-centric approach (Webster & Watson, 2002 ) due to the inclusion of four streams of literature: GBS, CRM, service innovation and business models.

As Webster and Watson ( 2002 ) envisaged, the research process started with a protocol development to create a defined body of literature for the theoretical development of a responsive business innovation model. The protocol had three phases, as depicted in Fig.  1 . The first phase mitigated the incompleteness risk of the literature review by systematically identifying and reviewing existing databases. While the second phase remedied the overlap from different databases by filtering for duplicates, the final phase focused on creating a consistent structure among all patterns. There was rigorous screening and appraisal of each paper to assess whether its content was fundamentally relevant. A final sample of 79 high-quality articles was selected to build the theoretical constructs for this study. Other articles published by technology or accounting firms in this paper’s literature review and results section were used to establish current market practices. Whittemore and Knafl ( 2005 ) stated that the suppositions of the integrative review could be reported in tabular or diagrammatic form. Since the study intended to develop a theoretical business model in the form of a diagram, a thematic analysis was used to consolidate further and conceptualise higher levels of themes, constructs, patterns and descriptions from articles associated with GBS, CRM, service innovation technologies and business models.

Source: Author

Phases of the integrative review.

Literature review

A theoretical framing is required for constructing a response business model. A business model provides a rationale, design or architecture for strategic choices to create, deliver and capture value (Magretta, 2002 ; Osterwalder & Pigneur, 2010 ) by specifying the structural elements and technology to address the unmet needs and activities of customers (Teece, 2018 ). Accordingly, organisational theory (strategic decision-making), customer relationship management (customer needs), global business service (structure) and service innovation technology provide the grounding for this research.

Organisational theories

The institutional theory provides a multifaceted business outlook on normative pressures from external and internal sources that influence organisational decision-making (Zucker, 1987 ). It determines conventional rules and assumptions (Oliver, 1997 ), whereby conformance to these norms is compensated through improved legitimacy, resources and survival capabilities (Scott, 1987 ). Institutions provide social structures, rules and resources that are important to the service sector. Adopting AI in the service sector differentiates the fourth industrial revolution from the third (Schwab, 2017 ), which triggers adaptive structural processes that progressively change the organisation’s social interaction rules and resources that determine decision efficiency outcomes (DeSanctis & Poole, 1994 ). In the knowledge economy Footnote 7 (Powell & Snellman, 2004 ), greater reliance is placed on the intellectual capabilities of intangible resources as opposed to physical resources for decision-efficiency outcomes.

Extrapolating these theories to the fourth industrial revolution, it is apparent that there are challenges that organisations face to conform to the normative pressures of digital disruption that depend upon each company’s specific circumstances (contingencies). “ A good business model begins with an insight into human motivations and ends in a rich stream of profits ” (Magretta, 2002  pg. 3). Each organisation needs to find a strategic fit within the knowledge economy to gain value-driving opportunities while accelerating its customer-centric initiatives. For this reason, the customer relationship management (CRM) literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections.

Customer relationship management

The core of CRM is understanding customer needs and leveraging that knowledge to increase a firm’s long-term profitability (Stringfellow et al., 2004 ). In the digital era, technology may be leveraged to be customer focused to understand customer needs better. For instance, probing large data sets (big data) may inform CRM strategies (Payne & Frow, 2005 ; Stringfellow et al., 2004 ). Customer data is a rich source of unstructured, voluminous and ambiguous data for further processing through analytics. Data analytics are recommended for managerial strategic decision-making, since it is grounded in evidence rather than perception (IBA Global Employment Institute, 2017 ; McAfee, et al., 2012 ). Knowledge gained from data analytics is essential for building close customer relationships for service differentiation, customer loyalty and value creation.

Irrespective of the industry, the desire to nurture customers is a key success factor driving the need for CRM differentiators to gain a strategic competitive advantage. However, Stringfellow et al. ( 2004 ) criticised knowledge-deficient models developed from superficial customer data (demographics and transactions), since these do not address the functional (purpose-fulfilling) and emotional requirements of customers. They used the study by Schneider and Bowen (1999) to illustrate that decision-making is not dictated by functional needs, since a man may pay double the price to buy a Ralph Lauren polo shirt instead of a similar unbranded polo shirt to fulfil his self-esteem needs. This diversity in customer decision-making illustrates that relational selling may sometimes outweigh value-based selling. Therefore, any customer-centric business model should understand that buyers are not always rational but emotionally guided. For this reason, sales or services can be categorised as value-based to fulfil purpose or relational to fulfil the emotional connections to the product or service.

Global business services

According to OECD ( 2007 ), business services are provided to other businesses instead of customers. Organisations wanting to reduce costs enter the outsourcing market for lower-cost business services. However, within a GBS, various processes and functions are shared and operate unitedly instead of using several shared service centres and dealing with outsourcing vendors independently. The principal objective of GBS is to provide business-to-business services at a reduced fee and at contracted levels of quality that improve practice through lean, cost-competitive, efficient and streamlined processes with an optimised cost structure (Daub et al., 2017 ; OECD, 2007 ; SSON, 2018 ). This goal is achieved by leveraging a range of enablers, including a robust customer interaction framework, standardisation, economies of scale, automation, organisational realignment, labour/robotic arbitrage, implementation of best practices and true “end-to-end” process optimisation (SSON, 2018 ). Thus, companies leverage a GBS model to gain market advantage and operational efficiencies through an agile, focused and leaner service organisation. GBS integrates services that forsake functional silos and transcends to a multifunctional collaborative approach. GBS has an amalgamated delivery model providing “back-office” services to a global customer base, such as accounting, finance, HR, IT and procurement, and increasingly moving to “front office” activities, such as sales, marketing, analytics and reporting (SSON, 2018 ). Currently, businesses are focussed on services related to their digital offerings and the analytics of their customers’ data. Geographical expansion, innovation quest and the adoption of new technologies are important in pursuing profits when competition is rife (Hodgson, 2003 ). GBS, with AI technology, has an opportunity to achieve scalability by integrating its multitude of centres into a single network to expand its range of business across the globe for a competitive advantage.

Most GBS users depend heavily upon intangible assets, particularly technological and service innovations (OECD, 2007 ). GBS centres can integrate automation, virtualisation and analytics, amongst other digital tools and capabilities, into their prevailing processes that provide more effective support to business units (Daub et al., 2017 ). Global organisations, such as Siemens, have incorporated a GBS-type structure into their global multifunctional business model that provides shared services to all Siemens businesses. The two fundamental principles that guide this organisation’s international services centres are customer satisfaction and continuous improvement through innovation (Siemens, 2020 ). For this reason, the GBS-type structure has extended to accounting firms, with their large global networks increasingly centralising certain remote auditing functions through technology and then outsourcing geographic-dependent work to their component auditors. For the longevity of any business, new organisational designs need to evolve that shape human workers, such as service innovation technologies.

Service innovation technologies

The innovation theory proposes that innovations diffuse from early adoption to widespread use (Rogers, 1995 ). However, innovations have a lag effect on their relative advantage (profitability, social prestige, other benefits) over its predecessor. In defining a technology readiness index ranging from innovators to laggards, Rogers ( 1995 ) elaborated on the speed of the adoption being positively related to the perceived benefits, compatibility with the company’s structures, ease of use and trialability (experimental capability). The innovation diffuses at the rate at which an innovation’s results are visible to others (observability). However, the complexity of the innovation is negatively related to the speed of the adoption. Understanding innovation theory is central to constructing or transforming a business model.

The quadruple-helix theory proposes that society can drive the innovation process to design sustainable strategies to achieve social innovations in a green economy (Carayannis et al., 2012 , 2020 ). ESG goals are increasingly being demanded by stakeholders to be incorporated into business models. The focus on ESG has led to traditional business models integrating sustainability while undergoing digital transformation. A sustainable business model delivers multifaceted value to a wider range of stakeholders when compared to the traditional business model (Bocken, et al., 2013 ). Digital technologies allow for strategic planning on economic, social, and environmental performance (Evans, et al., 2017 ). For instance, social network platforms may assist companies in achieving their ESG goals allowing companies to move closer to a green economy. Platforms are technologies that facilitate networking for companies to co-create with stakeholders (Allen, et al., 2009 ). A concept is drawn from the microworking philosophy (Howe, 2008 ), where a large dynamic network enables the organisation to connect with the internal and external environment for co-creation opportunities. Close company–customer collaboration allows for long-term value co-creation (Kamalaldin, et al., 2020 ), where customers co-produce services by providing insights. Types of co-creation opportunities are the wisdom of crowds Footnote 8 (Surowiecki, 2004 ), open innovation Footnote 9 (Chesbrough, 2003 ), crowdsourcing Footnote 10 (Howe, 2008 ) and crowdworking Footnote 11 (Ross, 2010 ). A common feature of these co-creation opportunities is that they all use an open call for knowledge to create innovative solutions. Amazon Mechanical Turk and Uber are examples of the crowdworking philosophy using digital platforms to build networks in the service sector. Leveraging society’s connectivity and responsiveness through platforms facilitates the collaborative designing of personalised products, services and experiences.

Technologies such as RPA and service bots have been widely adopted in the service industry. RPA interacts with the user interface of other computer systems using rule/logic-driven software robots (softbots) that are coded to execute a high volume of repetitive tasks without compromising the underlying IT infrastructure (Deloitte, 2018 ; van der Aalst et al., 2018 ; Willcocks et al., 2015 ). This technology dates to the Eliza programme’s interactive bots that enabled interaction between humans and machines using text-based communication (known as the Turing test) (Turing, 1950 ; Weizenbaum 1966 ). RPA follows prescribed protocols and procedures that increase the speed, accuracy, compliance and productivity of business processes. Footnote 12 Instead of multiple ERP solutions (taking data from one system and inputting it into another system), it is more cost-effective and efficient to integrate RPA into a company’s existing infrastructure and automate processes (van der Aalst et al., 2018 ). However, RPA is on the lower end of intelligent automation, since it uses structured logic and inputs to operate from simple to complex business tasks.

RPA with cognitive automation has allowed softbots to be more useful due to their superior intelligence. Softbots with machine learning Footnote 13 capabilities are designed to mimic human thought and action to manage and analyse big data with greater speed, accuracy and consistency than humans can achieve by leveraging different algorithms and technological approaches (Firstsource, 2019 ). Algorithms do not produce definitive solutions but present probability-based predictions for humans to evaluate and make informed decisions. Table 1 provides a summary of the Softbots.

Softbots are also known as service robots, chatbots, AI bots, AI assistants, virtual assistants or agents, and digital assistants or agents. This study adopts the term service robots, since they are most common in customer support or sales environments, where they are expected to serve customers. For instance, call centre jobs are labour-intensive and employing people’ around the clock’ for one or two late-night phone calls are costly. However, service bots can answer simple queries efficiently and far quicker than a person can. Service bots use Natural Language Processing (NLP) to develop logic from unstructured inputs for human interaction. Service bots with NLP, Natural Language Understanding (NLU) Footnote 14 and Natural Language Generation (NLG) Footnote 15 are distinguished from the greater domain of service bots due to their aptitude to employ language to converse with their clients. Table 2 shows the different types of service bots.

Kiat ( 2017 ) states that service bots can manage CRM quality by handling mundane tasks leaving salespeople to focus on high-value tasks, such as meeting customers and concluding company sales. In general, leads should be attended to within 5 min to convert them to paying customers, which would be achieved with service bots. Other advantages are:

Seamless interface: bots can recall their previous customer interactions and seamlessly verify customer data by linking to social media, so queries are addressed at a speed unmatched by humans. Service bots can also seamlessly transfer complicated cases to human operators, facilitating humans’ foci on higher value customer engagements.

Data enrichment: cost-effectively resolving data leakage problems, since humans often neglect to record key customer information from the various stages of the customer’s purchase process, whereas a service bot would automatically capture the discussion.

Service bots are key differentiators within the IT industry with improved revenue performance and customer value (customer contentment, service delivery and contact centre performance) (MIT Technology Review, 2018 ). Service innovation technologies are employed by renowned brands, such as Amazon, Netflix, Starbucks and Spotify, to name a few. Service bots work reliably and accurately around the clock while maintaining the same competence level without being distracted or fatigued. Service bots also do not have inherent limitations, such as becoming ill, going on strike or requiring leave. In 2019, the banking sector achieved operational cost savings of $209 million from employing service bots. Insurance claims management departments had cost savings of $300 million across motor, life, property and health insurance (Juniper Research, 2019 ). Artificial Solutions ( 2020 ) also reported that Shell attained a 40 per cent decrease in call volume to live agents due to their service bots, Emma and Ethan. They answered 97 per cent of questions correctly and resolved 74 per cent of digital dialogues. Similarly, the service bot Laura is digitally transforming Skoda (a Volkswagen Group’s subsidiary), where customers can discuss their vehicle needs and budget with Laura (Artificial Solutions, 2020 ). Therefore, digitalisation has resulted in customer relationships evolving from transactional to more relational.

Results: theoretical propositions

Several constructs emerged from the thematic analysis of the integrative review for developing a digital business model, reflected in Table 3 .

Using the people, process and technology (PPT) framework (Leavitt, 1964 ), these ten constructs from Table 3 and innovation capabilities are presented in Fig.  2 . This study has added governance to the PPT framework to form the PPTG framework. Governance is imperative for oversight over the value-creating activities (Sewpersadh, 2019a ) to balance the trade-offs from the synergistic benefits of lower costs, increased coordination, greater productivity and value delivery with the ethical and risk concerns over customer data.

PPTG Framework.

In Fig.  2 , people have been expanded to include service bots. Collaboration between service bots, employees and customers are integral for value co-creation. Service bots cost-effectively record customer information from the various stages of their service interactions, allowing for data warehousing. Data warehousing is important for allowing data mining tools and the analysis of critical customer parameters.An ethics and risk officer will play a key governance role in overseeing the principles of fairness and ethics over emerging technologies, such as service bots. Increasingly companies integrate their AI technologies with social media platforms which necessitates the ethics and risk officer to detect, correct and prevent any biases that the service bots learn through the data they collect. For example, service bots may discriminate against customers based on their demographics (Puntoni et al., 2021 ). In 2016, Microsoft launched a service bot called Tay to research conversational understanding. This project failed, because the developers did not anticipate that some Twitter users would teach the bot to make racist, inflammatory and offensive tweets through its Twitter account (Berditchevskaia & Baeck, 2020 ). For this reason, recent studies proposed digital corporate responsibility to guide ethical dilemmas related to AI technology (Lobschat et al., 2021 ). There are also ethical and security risks when service bots impersonate humans (van der Aalst et al., 2018 ), since they may make improper judgements due to contextual changes that may remain undetected, leading to unintended consequences. For instance, service bots may make poor-quality recommendations that do not align with customer interests or may expose customers to vulnerable and risky situations (Mullainathan & Obermeyer, 2017 ). Service bots require service audits to prevent poor service quality outcomes. Service bots also have excessive access and privileges that place them at risk of cyber-attacks. The ethics and risk officer may assist in safeguarding data using surveillance methods to detect intelligent malware. Footnote 16 Research has found that customers are more likely to act unethically and misbehave (LaMothe & Bobek, 2020 ) when interacting with service bots. Therefore, service bots need to be monitored to detect and prevent these infringements.

In Fig.  2 , PPTG is improved with technologies for process value configuration. Technology with people allows for smart analytics on service value capture and optimisation. For example, service staff, key accounts managers and digital developers in Solutioncorp evaluate customer service data to identify priority areas for AI innovation (Sjödin et al., 2021 ). This dispersion of emerging technology gives rise to a disruptive landscape in the knowledge economy, necessitating more R&D and continual business model innovation. The three overarching themes from the constructs presented in Table 3 are innovation, sustainable business models and value creation, which will be discussed further below.

Innovation continuum

The rapid pace of the evolution in technology innovation accelerates the diffusion of innovations (Rogers, 1995 ). The increased R&D in innovation creates a continuum (Fig.  3 ), where companies are not statically classified according to their degree of innovation but rather placed on a continuum. Those businesses that recognise innovations’ relative advantages, compatibility and trialability (Rogers, 1995 ) will move to the higher end of the continuum. Although, a high-innovation company may not remain a disruptor in the market if it becomes complacent or myopic with its innovation strategy and neglects to continuously improve its business processes. This complacency can be explained by the icarus paradox, where success may lead to a path of convergence with an emphasis on the same strategies, which may simplify and desensitise divergent evolving demands (Elsass, 1993 ; Miller, 1990 ). Past successes promote a defensive mindset and overconfidence, resulting in the persistence of the same strategic formulas when executing innovative strategies is the most appropriate response (Sewpersadh, 2019b ) to the market’s changing needs. Thus, this paradox may lead to myopia, complacency and inertia. This complacency leads to a condition of ‘unconscious incompetence’, where the lack of knowledge of the availability of advanced technologies leads to suboptimal decision-making or decision paralysis on deploying such technologies. For this reason, the degree of innovation is bidirectional on the innovation continuum, which allows for the acceleration and deceleration of innovation investment. As business models transition from traditional to transformative ones, eventually evolving into disruptive ones, those with myopic capabilities soon find their business models antiquated. When companies intensify their investment in innovation, they adopt a futurist strategy allowing them to transition up the innovation continuum and challenge complacent companies.

Innovation Continuum.

Rogers ( 1995 ) cautioned that insufficient knowledge, inability to predict consequences or overzealous innovation investments might lead to over-adoption. Also, the complexity or incompatibility of innovations may not be suitable for some businesses, which may jeopardise their positioning on the continuum. For this reason, governance structures, such as a digitalisation committee, are important for moderating the firm’s adoption strategy. This committee will assess the suitability, acceptability, feasibility and sustainability of developing or acquiring innovations. Integrating stakeholder networks in collaborative activities creates trust-based relationships, legitimacy and good governance that allows for the acceptability of innovations. In Fig.  3 , governance optimisation is vital for ensuring value-maximising decision-making concerning value-creating activities for all stakeholders (Sewpersadh, 2019a ).

There could also be a reluctancy to allocate resources for R&D due to a digital paradox (revenue growth is not as expected despite the proven growth potential) (Gebauer, et al., 2020 ). For these reasons, value creation and governance optimisation are unidirectional factors in Fig.  3 and are placed on the high end of the continuum, where disruptive business models operate. Governance is essential to moderate the negative effects of an over-adoption, complex or incompatible innovations and the digital paradox. Good governance is also critical for balancing trade-offs when making strategic decisions. For instance, harmonising the need for legally protected intellectual assets for profit maximisation and sustainability with knowledge sharing to build collaborative networks.

Central to the innovation process is the need for firms to create and acquire “new combinations” of knowledge. Based on the resource-based theory, complementary assets and capabilities are scarce but valuable strategic resources, since they have strong path dependencies that are difficult to imitate (Barney, 1991 ), thus shaping the firm’s competitive advantage in the cooperative network. Since companies compete in a capital-intensive space, with barriers to entry and economies of scale, profits may be achieved with the legal protection of competitive advantages, such as closed innovation. Closed innovation is the internal research within a particular company that is generally protected by patents, so that access to that innovation is controlled by the rightsholder (Chesbrough, 2003 ). Progressively, open innovation has become a way in which key resources are obtained for the development and execution of innovation (Chesbrough, 2003 , 2011 ). Open innovation is a means of sharing costs, ideas, synergies and skills (Chesbrough & Crowther, 2006 ) from value networks to co-create innovation rather than an individual company outlaying capital to conduct R&D from scratch. For this reason, in Fig.  3 , the networking capabilities of a company also follow the direction of its innovation policy due to the collaborative work with extended networks that allow for the acquisition of external knowledge. As innovation diffuses, collaborators within forged networks stimulate newer co-created innovations with superior outcomes.

A significant limitation to knowledge sharing is the disclosure of internal knowledge to external collaborators (Cassiman & Veugelers, 2002 ), commonly referred to as the risk of knowledge leakage (Gans & Stern, 2003 ) or the “paradox of openness” (Laursen & Salter, 2014 ). This paradox describes the fundamental tension between knowledge sharing (value creation) and knowledge protection (value appropriation) in open innovation. Open innovation may increase the imitation tendency of mimetic companies, who benefit from incurring fewer costs and inefficiencies with access to extended networks. Therefore, a company’s position on the continuum and its competitive stance in the industry depends upon its ability to remain at the technological forefront. Consequently, open innovation also poses significant governance challenges to monitoring, controlling, and managing intellectual property rights in enterprise innovation (Graham & Mowery, 2006 ). Hence, risk-averse companies usually have linear business models with a unilateral dependency on internal resources. This tendency to be an information hoarder lends itself to a closed innovation competitive stance. For this reason, the company’s risk strategy must also be considered, since innovation pioneers may be more risk-tolerant than those with more traditional business models. As newer, more revolutionary technologies become available, static business models with poor networks risk being on the low end of the innovation continuum. Companies that have failed to keep at the forefront of technology do not have sustainable business models and may lose their extended networks.

Sustainable business models

The diminishing competitiveness of traditional business models (McGrath, 2010 ) has led to a fundamental rethinking of the firm’s value proposition for new prospects (Bock et al., 2012 ) on refining how an existing product or service is provided to the customer (Velu & Stiles, 2013 ). Reconceptualising structural elements for technology and resource capitalisation to create new activity frameworks and networks aimed at clear value propositions is known as business model innovation (Battistella et al., 2017 ; Hamel, 2000 ; Helfat et al., 2007 ). Therefore, business model responsiveness becomes a critical success factor in addressing challenges in the knowledge economy. A business model’s alignment and coherence should be mutually reinforcing and incorporate a response to the concomitant influence of contextual factors (Dehning & Richardson, 2002 ; Melville et al., 2004 ; Schryen, 2013 ) and lag effects on firm performance (Schryen, 2013 ). The responsive business innovation model, in Fig.  4 is a hybridisation of prior value models with interlinkages to current service technologies employed in the market, including digital platforms, crowdsourcing, blockchain, crowdworking, big data and service bots.

Responsive Business Innovation Model.

Figure  4 ascribes to Santos et al. ( 2015 ), where the model is more about “how is it being done?” than “what is being done? It incorporates an iterative strategy that maps cross-functional relationships between innovations and the underlying activities to be responsive to the evolving economic environment. Large corporates often use share centre services to support their network of firms under a GBS structure. However, with the evolution of AI, the GBS structure can evolve into a digital platform business model. A responsive business innovation model focuses on facilitating interactions across many shared centres by providing a governance structure and a set of standards, so that they operate as one cohesive ecosystem. It is an activity system with interconnected and interdependent activities to satisfy the market’s perceived needs (Foss & Saebi, 2018 ).

The responsive business innovation model enables the acquiring, developing, and integrating of key resources to overcome inertia. Introducing a new business model into an existing organisation is challenging and may require a separate organisational unit to redefine and reconfigure the model. For example, General Electric (GE) experienced business model transformation conflicts when they tried to adopt digital servitisation. There were conflicts between digital and physical service offerings, new ecosystem partnerships and traditional supply chain relationships, digital revenue and product sale models (Moazed, 2018 ). For this reason, positioning a Centre of Excellence (COE) is important, since it can provide the organisational structure, methodology, skills, tools and governance framework for handling the future innovation needs of a large global corporate (SSON, 2018 ). A GBS structure includes a COE for higher level business support and specialist work and thus is incorporated in Fig.  4 . COE comprise of a centralised specialist team to promote collaboration and provide higher value services, resulting in economies of scale. COEs focus on agility, Footnote 17 CRM and talent development while standardising and automating cross-function end-to-end process ownership), resulting in reducing costs and harnessing process efficiency (SSON, 2018 ). Examples of these are procure-to-pay (supply chain and accounting) and hire-to-retire (HR and accounting. The positioning of the GBS is better placed by groups of talent (area of expertise) rather than location, function or lowest costs.

The CRM literature provides a framework to delve into human motivations concerning their buying incentives, biases and emotional connections. For this reason, CRM is at the heart of the business model with AI differentiators (McAfee et al., 2012 ; Payne & Frow, 2005 ; Stringfellow et al., 2004 ) that responds to evolving consumer behaviour and expectations. The deep knowledge of consumers’ emotional and functional needs allows businesses to optimise capital to address those needs. This strategic response to customer needs and experience requires standardisation (lower costs, benchmark service quality) and differentiation (premium service). For instance, businesses could standardise business processes through RPA for efficiency gains but personalise services via service bots for market differentiation.

Service bots are key components of a digital strategy for entities searching for innovative and cost-effective means to build closer customer relationships (Artificial Solutions, 2020 ). With a GBS structure, the service bots may need to be multilingual due to the diversified client base. Furthermore, by integrating with social media (shown in Fig.  4 ), service bots can access clients’ online data and learn their preferences, sentiments, outlooks and proclivities. The data from clients’ online presence are often undervalued, but access to this enables businesses to transcend beyond basic business intelligence. Therefore, the service bot’s initial customer interaction will offer a superior service through seamless verification of personal information (similar to the Facebook sign-up process) and quick information transfer through hyperlinks. A seamless trail of conversations can be achieved whenever users swap from device to device (cross-platform Footnote 18 ), since this practice improves engagement and customer fulfilment (Artificial Solutions, 2020 ). The increased customer engagement means more actionable and enriched data to train service bots to personalise the customer’s experience. In so doing, service bots can service customers more competently and cost-effectively without human error (Artificial Solutions, 2020 ; Kiat, 2017 ).

A limitation of service bots is that humans can notice tone and subtext in a way that a service bot could never master. This disparity calls for cross-functional collaboration between service bots and higher skilled humans, transitioning toward blended workforces. Data-centric CRM harness the potential of big data to focus on not only the functional but also the deeper psychological aspects of buying behaviour (Stringfellow et al., 2004 ). Access to client data is essential for value creation (Paiola & Gebauer, 2020 ) to improve existing services and create novel innovations (Opresnik & Taisch, 2015 ) within the confines of privacy laws. Automating customer interaction with service bots (see Fig.  4 ) allows for a higher degree of message personalisation without increasing personnel costs. In-depth analysis of unstructured conversational data conveys perceptions on what is done well or what can be improved by the business to develop market differentiators for a strategic competitive advantage. Smart analytics, such as sentiment analysis, support businesses in gauging their customers’ mindsets Footnote 19 and analysing the customer’s journey more effectively while remaining within the confines of data safety legislation.

Strategy guides and shapes by including the company’s brand reputation, Fig.  4 . The iterative CRM engagement strategy and value outlook (short, medium and long term) is built from big data collected from the AI-led CRM and crowdsourcing from their networks. This process allows companies to leverage their large network of end-users to inform the co-created products, services and experiences. A large network also provides microwork opportunities through crowd-working platforms for comprehensive support and supplement human labour. However, managing the trade-offs between stakeholders, technology, and societal benefits is important. Stakeholder engagement is essential in identifying key stakeholder requirements for these benefits to occur. Accordingly, business models should recognise and incorporate environmental, social and governance (ESG) goals, whereby trade-offs must be managed. For instance, automation disrupts the human capital leverage model, in which a trade-off exists between harmonising the prospective savings from automation and the human impact of job losses. Due to the escalation of global warming, business models must also incorporate innovative sustainable environmental solutions (Carayannis et al., 2020 ). Therefore, innovations must be expanded beyond service innovations to ESG innovations.

In Fig.  4 , the benefits of using blockchain technology in a business model are also presented. Blockchain represents an endlessly accumulating list of records stored in “blocks” protected using cryptography principles (Arnaut & Bećirović, 2020 ). The peer-to-peer protocol ensures unambiguous and common ordering of all transactions in blocks, a process that guarantees consistency, decentralisation, integrity and auditability (Arnaut & Bećirović, 2020 ; Yuan & Wang, 2018 ). These features make the blockchain’s permanent ledger resistant to data manipulation, which is a value contribution to the company.

Value creation

A business model’s lifecycle involves “periods of specification, refinement, adaptation, revision and reformulation” (Morris et al., 2005  pg.732). The business model’s initial period in the lifecycle has a process of trial and error, where core decision-making delimits the firm’s evolution. For this reason, a value creation cycle is essential to harness a sustainable competitive advantage by continuously refining, adapting, revising and reformulating a business model to counteract the limitation of becoming static. In Fig.  5 , the importance of the continual assessment of the contextual factors, and the suitability thereof, feed into the value creation cycle necessitating the need for change. However, the suitability of this change must be assessed in terms of the company’s contingencies. Research is necessary for informed decision-making on whether the change is incremental versus transformative to reap all the benefits and value that innovations offer. For value creation, the decision-making process should be free from bias and consider the business’s ESG values, goals, and trade-offs. It is also important to be cognisant that there is a time lag before benefits can be realised. A value architecture may also assist in alleviating some of the trade-offs, particularly structuring a digitalisation committee.

Value creation cycle.

The value architecture (Osterwalder & Pigneur, 2010 ), presented in Fig.  6 , allows a responsive business innovation model to capture and create market activation to build the deep, compelling experiences customers desire with service-related products. However, there is a need to balance the trade-offs between conflicting value drivers. For instance, costly R&D may have environmental consequences that conflict with the desire to provide a good return on capital. For this reason, a clear value preposition is the first step in the value architecture. A value preposition is the underlying economic logic explaining how value is delivered to customers at the appropriate cost (Magretta, 2002 ). The building blocks of value proposition, configuration, delivery and capture (Osterwalder & Pigneur, 2010 ; Osterwalder et al., 2005 ) must be considered to develop a sustainable competitive advantage for the organisation (Teece, 2010 ). While the value preposition remains customer centred, the value configuration and capture are focused on relational selling using technological innovations. While the value delivery is focused on efficiency and service optimisation using service innovations.

Value Architecture.

With the global environment moving so swiftly, multidisciplinary research is necessary to condense and intensify business knowledge. This study highlights the need to examine the discontinuous shift in the scope and culture of business models by exploring interdisciplinary streams of literature. An analysis of the recent literature revealed a lack of research fusing automated technologies in the business models of CRM-intensive companies. This study bridged the theoretical frameworks of organisational theories to learn how contingent characteristics influence the design and function of business models. A key contribution was the inclusion of structural elements (GBS, CRM and AI) to design a responsive business innovation model to create, deliver and capture value. It was established that AI-led CRM in a GBS structure yields a greater focus on generating innovative services that satisfy customers’ emerging needs as well as balance ESG goals. Instead of just customers just being consumers, they can be strategic networks to collaborate and co-create outcomes by integrating CRM and AI technologies into a GBS structure.

Global businesses must update their cost focussed models to transcend into the digital age by moving forward on the innovation continuum model and refocussing on customer-centric service innovations to thrive in this evolving environment. An over-reliance on past successful formulae and static business models leads to the eventual demise of AI-complacent companies. A prime example was seen during the COVID-19 pandemic when some businesses adapted swiftly to the enforced lockdowns using more digital avenues of earning revenue, while others failed to advance up the innovation continuum and closed their businesses, resulting in the loss of millions of jobs. The COVID-19 pandemic is not the only crisis faced by the global economy, since there have been other life-threatening epidemics, such as the Zika virus, MERS, Swine flu, SARS, Aids and Ebola. Businesses need to adapt to the ever-changing environment with cognitive flexibility and agility to transform their business in the wake of any crisis. Structures such as the COE may assist companies in averting the risk of unconscious incompetence in respect of evolving AI and place them at the forefront of the innovation continuum for sustained viability. Static business models can use existing digital platforms to enhance their services, enabling them to move up the innovation continuum. These businesses will have collaboration and co-creation opportunities from the large networks on the high end of the innovation continuum.

This article illustrated the benefits of AI, specifically how service bots can assist in creating new and improved business models in business-to-business and business-to-consumer markets with CRM adoption. Since service bots are a market differentiator, businesses at the forefront of service innovation are assured of resilience, even when faced with the threat of a pandemic. Service bots use real-time data to predict and influence customer behaviour, preferences, buying incentives, and spending tendencies. The un-leveraging of the human capital model has accelerated at an unprecedented level amid the COVID-19 pandemic and is foreseen as being at its most impactful in the post-pandemic period. The effects of AI technology on the human capital leverage model vary depending upon humans’ skills set. AI technology is negatively associated with low-skilled workers but significantly positively influences highly skilled workers.

Multinationals have better opportunities than single-country competitors to experiment with various business models in different geographies and then transfer those validated models to all geographies in which they can capture value (Teece, 2014 ). In the digital transformation era, customer-centricity and global marketplace competition, shared services have evolved from outsourcing to in-housing/re-shoring a GBS model for developing a single and consistent approach to providing internal customer services across functions and geographies. For GBS to stay at the forefront of service delivery development and remain competitive, GBS leaders must leverage and scale these new technologies. GBS’s global reach and governance, standardised processes, extended business process ownership and use of consistent operating models and technologies make them ideal candidates for implementing and delivering the aforementioned AI arbitrage benefits for their operations. This study has illustrated the tremendous strides made in AI technologies, whereby AI investment does not comprise resource-depleting disbursements but encompasses intangible assets through which the system autonomously learns and continually advances. These digital avenues provide key market differentiators in customer service.

Management cannot rely exclusively on in-house expertise and needs the benefits of mechanisms, such as crowdsourcing and crowdworking, to create a comprehensive sustainable business model. However, regulators need to be wary of the potential ramifications of crowdsourcing and crowdworking, since opportunistic companies may exploit these platforms for cheap labour. Blockchain must be considered when proposing disruptive models due to its revolutionary potential. As businesses move to scale their digital ingenuities, a focus is placed on the agility to respond to consumers’ evolving tastes with diminishing lag times due to the availability of real-time data.

Inevitable changes in business models are necessary as organisations shift how they create, capture and deliver value. For these reasons, this study developed key value drivers grounded in the theoretical framework. The key findings of this article are the various conflicting trade-offs between value drivers and ESG goals in digital business models that require executives to harmonise. Some examples of these trade-offs were:

the societal impacts of human job losses conflict with the efficiency and cost benefits of cognitive automation,

the utilisation of customer conversational data conflicts with remaining within the confines of data protection legislature,

the cost of software intrusion detection systems to avoid losing confidential data conflicts with the desire to maintain profitability margins,

the cost of innovation R&D conflicts with the desire to provide a good return on capital, and

the standardisation of processes conflicts with the customisation of services to avoid the loss of strategic competitive advantage.

This study identified governance as a key mechanism in managing ethical issues and risks. Concerns about consumer privacy may cause governments to prevent some important innovative developments in global services (World Trade Organization, 2019 ). Data security is a crucial concern for any business due to security risks when handling customers’ personal information. For example, in 2018, Facebook was guilty of invading users’ personal data and giving this information to other large corporations, such as Amazon, Microsoft and Spotify, to increase Facebook’s users and revenue (Dance et al., 2018 ). Although regulatory user protection laws exist, businesses must employ centralised data management with cognitive analytics capabilities, encryptions, independent security audits and codes of practice. Personal identifiable data is a highly valuable commodity in the digital age but is also unsafe, since any data breaches will result in customers losing trust. Kelley ( 2019 ) recommends that a successful security protocol is to program service bots to identify personal and/or sensitive information and treat it accordingly. Systems must be able to anonymise or pseudonymise conversational data, replacing identifiable data with placeholders, so users can still understand the intent for analytics purposes but not know the customers’ identity (Kelley, 2019 ). Despite the challenges of surveillance and privacy issues, digital technologies are increasingly central to people, organisations and societies (Flyverbom et al., 2019 ). For instance, the UK government has invested more than £1 billion into an AI industrial strategy (Berditchevskaia & Baeck, 2020 ), thus, illustrating that some countries have grasped the opportunity to build value-added resiliency into a service delivery model.

Recommendations

Companies should be aware of their business model lifecycle to avoid becoming stagnant. Therefore, it is recommended that they adopt a responsive business innovation model with a value-creating cycle to continuously refine, adapt, revise and reformulate their business model. To achieve this, companies should also have an innovation strategy driving a customer-centric service innovation culture while reducing costs and leveraging the finest skills. Organisations should consider establishing a COE with an innovation leader to be at the forefront of innovative technologies.

The COE would seize, assess and manage cognitive automation technologies for data governance. The COE is vital for providing leadership, driving change, and influencing business strategy and multiple onboard stakeholders across the business. COE’s essential function is driving an automation strategy as follows:

Develop an iterative strategy to extend and expand existing capabilities through automation.

Drive a holistic AI-enabled disruptive operating model, similar to the model proposed in this article, that is cost-efficient and leverages ‘fit-for-purpose’ technology to inspire ‘out-of-the-box’ thinking and nurture an entrepreneurial ethos.

Incorporate and harness a digital platform strategy management that accelerates the rate of digital platforms to realise cost savings and drive resiliency.

Initiate regular consolidating and mapping of business processes to identify areas of duplication and labour-intensive processes for an automation analysis to appraise potential benefits.

Create an AI-intensive GBS with an effective COE to use cognitive automation technologies in customer-centric service delivery.

Ensure CRM focuses on new customer onboarding forms and data-driven methods.

Benchmark against industry and competitors to ensure that the company’s technology has a competitive advantage.

Create consistent and frequent communication channels between COE and those charged with firm governance.

Design a data governance model to determine control and direct the use of data (how and for what purpose).

Create guidelines on data protection, privacy, intellectual property rights and ethical issues in data management.

It is also highly recommended that the public sector employs AI-intensive technologies, specifically RPA and service bots, that can streamline business processes. This sector’s work is extremely labour intensive, which is inefficient and resources depleting, given the recent rise in digital technologies. The large burden placed on taxpayers to supplement the ever-increasing public sector budgets is not met with improved outcomes. Lower level public officials’ mundane and repetitive work, such as capturing information from one system to another, using ineffective reporting templates and manual month-end tasks, are time-consuming, costly and widen the margin for human error. The public sector is also continuously dealing with fraud, tender bribes and schemes that impair its ability to deliver public services efficiently. The employment of digital agents can improve and expedite these laborious, inefficient and frustrating processes and, even more importantly, alleviate fraud to some degree.

Future research agenda

This study focused on the value of service innovation technologies in responsive business innovation models. However, there is an abundance of future research explorations in the list below, which is not exhaustive.

Service bots

Research that empirically tests the customers’ satisfaction journey with digital workers versus human workers, particularly from a customer demographic perspective. For instance, NLP has made strides in making service bots more humanlike. However, there needs to be research that interrogates which customer demographics are more amenable to service bot services and which are not. Furthermore, research needs to be conducted on service bots’ ability to match their customers’ evolving needs.

There should also be studies examining the emotional consequences on customers when their needs are addressed by service bots, particularly from a customer demographic perspective and any potential extensions to service bot biases.

Research examining customers’ concerns over privacy and data leakages and which service bot interactions are more likely to trigger these concerns.

Investigations into the potential impact on the company reputation/brand when faced with negative service bot interactions and biases, amongst others.

Research investigating potential trust or control issues when customers and employees rely on work performed by service bots.

Public service sectors

As with institutional theory, government intervention is also necessary for a functional digital ecosystem concerning infrastructure and access to funding and investment resources. Studies should investigate government funding structures to encourage more innovative R&D.

An appraisal of the public sector’s readiness for the digital transformation of their business model, since automated processes will result in societal benefits of service efficiency and tax savings for citizens.

An empirical study on the suitability of a GBS innovation model for the external audit service. Due to the nature of their service, there is potential for a suitable fit.

Open source/collaborative technologies

An investigation into the use of open innovation systems and collaborative platforms in assisting start-up companies with their digital transformation.

Availability of data and materials

Freely available using online research databases.

Digitalisation or digital transformation is the use of AI technology in the business processes and activities of a company.

AI is distinct from conventional information technology and is defined as the ability to learn, connect, assimilate and exhibit human intelligence.

Automation is defined as the employment of technologies to perform a process or task that reduces human intervention.

Innovation in terms of this research refers to business model, service and technological innovation.

A value proposition enables stakeholders to understand how the business intends to use its strategic resources, which is then mapped to the business model.

The transition from products and add-on services to smart solutions with connectivity, monitoring, control, optimisation and autonomy is known as digital servitisation.

The knowledge economy is defined as production and services based on knowledge-intensive activities that contribute to an accelerated pace of technological and scientific advance as well as equally rapid obsolescence (Powell & Snellman 2004  pg. 201).

Wisdom of crowds uses a wide range of annotators to create large datasets, for example, Wikipedia.

Open innovation is the free flow of knowledge to accelerate internal and external innovation.

Crowdsource is an open call to internet users to get innovative solutions.

Crowdwork is “the performance of tasks online by distributed crowd workers who are financially compensated by requesters (individuals, groups, or organizations)” (Kittur et al., 2013 pg. 1).

Business processes are activities that underly value-generating processes such as transforming inputs to outputs (Melville et al., 2004 ).

Machine learning allows a machine to learn by using algorithms to analyse and draw inferences from patterns in data without direct intervention.

NLU helps bots understand the user by using language objects (such as lexicons, synonyms and themes) in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.

NLG enables bots to interrogate data repositories, including integrated back-end systems and third-party databases for information to be used to create meaningful and personalised responses that are beyond pre-scripted responses.

Intelligent malware is AI-based and exploits vulnerabilities by mimicking normal user behaviour to avoid being detected.

Agility can be described as a dynamic process of anticipating or adjusting to trends and customer needs without diverging from the company vision (Fartash et al., 2012 ).

Cross-platforms recognise inter-relationships and complimentary services through different software applications and devices.

Mindset is the attitudes and norms that either inhibit or encourage people’s or firms’ decisions.

Abbreviations

• Artificial intelligence

Chief information officer

Centre of excellence

Coronavirus disease 2019

Enterprise resource planning

Environmental, social and governance

Information technology

Organisation for economic co-operation and development

Natural language understanding

Natural language generation

Natural language processing

Robotic process automation

Research and development

Service robot

Software robots

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33 (2), 3–30.

Article   Google Scholar  

Agostinelli, S., Marrella, A. & Mecella, M. (2020). Towards Intelligent Robotic Process Automation for BPMers. [Online] Available at: https://arxiv.org/pdf/2001.00804.pdf . [Accessed 24 April 2020]

Allen, S., Bailetti, T. & Tanev, S. (2009). Components of co-creation, s.l.: Open Source Business Resource. http://timreview.ca/article/301

Amit, R., & Zott, C. (2001). Value creation in E-business. Strategic Management Journal, 22 (6–7), 493–520.

Amit, R., & Zott, C. (2012). Creating value through business model innovation. MIT Sloan Management Review, 53 (3), 40–50.

Google Scholar  

Anderson, J., & Kupp, M. (2008). Serving the poor: Drivers of business model innovation in mobile. Info, 10 (1), 5–12.

Arnaut, D. & Bećirović, D. (2020). Empowering SMEs through blockchain based junior stock exchange. Tuzla, Visoka škola “Internacionalna poslovno-informaciona akademija” Tuzla.

Artificial Solutions. (2020). Chatbots: the definitive guide. [Online] Available at: https://www.artificial-solutions.com/chatbots . [Accessed 18 May 2020]

Aspara, J., Lamberg, J. A., Laukia, A., & Tikkanen, H. (2013). Corporate business model transformation and interorganisational cognition: The case of Nokia. Long Range Planning, 46 (6), 459–474.

Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17 (1), 99–120.

Battistella, C., De Toni, A. F., De Zan, G., & Pessot, E. (2017). Cultivating business model agility through focused capabilities: A multiple case study. Journal of Business Research, 73 , 65–82.

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1 (3), 311–320.

Berditchevskaia, A. & Baeck, P. (2020). The Future of Minds and Machines. [Online] Available at: https://media.nesta.org.uk/documents/FINAL_The_future_of_minds_and_machines.pdf . [Accessed 10 May 2020]

Bock, A. J., Opsahl, T., George, G., & Gann, D. M. (2012). The effects of culture and structure on strategic flexibility during business model innovation. Journal of Management Studies, 49 (2), 279–305.

Bocken, N. M. P., Short, S., Rana, P., & Evans, S. (2013). A value mapping tool for sustainable business modelling. Corporate Governance, 13 (5), 482–497.

Boons, F., Montalvo, C., Quist, J., & Wagner, M. (2013). Sustainable innovation, business models and economic performance: An overview. Journal of Cleaner Production, 45 , 1–8.

Bryman, A. (2012). Social Research Methods . Oxford University Press.

Carayannis, E. G., Acikdilli, G., & Ziemnowicz, C. (2020). Creative destruction in international trade: Insights from the quadruple and quintuple innovation Helix models. Journal of the Knowledge Economy, 11 (4), 1489–1508.

Carayannis, E. G., Barth, T. D., & Campbell, D. F. (2012). The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneurship, 1 (1), 1–12.

Casadesus-Masanell, R., & Zhu, F. (2013). Business model innovation and competitive imitation. The case of sponsor-based business models. Strategic Management Journal, 34 (4), 464–482.

Cassiman, B., & Veugelers, R. (2002). R&D cooperation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92 (4), 1169–1184.

Chesbrough, H. (2003). Open Innovation: The new imperative for creating and profiting from technology . Harvard University Press.

Chesbrough, H. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43 (2–3), 354–363.

Chesbrough, H. W. (2011). Bringing open innovation to services. MIT Sloan Management Review, 52 (2), 85–91.

Chesbrough, H., & Crowther, A. K. (2006). Beyond high tech: Early adopters of open innovation in other industries. R&d Management, 36 (3), 229–236.

Coltman, T., Tallon, P., Sharma, R., & Queiroz, M. (2015). Strategic IT alignment: Twenty-five years on. Journal of Information Technology, 30 , 91–100.

Dance, G. J., LaForgia, M. & Confessore, N. (2018). As Facebook Raised a Privacy Wall, It Carved an Opening for Tech Gaints The New York Times. [Online] Available at: https://www.nytimes.com/2018/12/18/technology/facebook-privacy.html . [Accessed 15 April 2020]

Daub, M., Ess, A., Silver, J. & Singh, S. (2017). Does the global business services model still matter?. [Online] Available at: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/does-the-global-business-services-model-still-matter . [Accessed 21 April 2020]

Dedrick, J., Gurbaxani, V., & Kraemer, K. (2003). Information technology and economic performance: A critical review of the empirical evidence. ACM Computing Surveys (CSUR), 35 (1), 1–28.

Dehning, B., & Richardson, V. (2002). Returns on investments in information technology: A research synthesis. Journal of Information Systems, 16 (1), 7–30.

Deloitte. (2018). Robotic process automation . Deloitte.

DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organisation Science, 5 (2), 121–147.

Downes, L., & Nunes, P. (2013). Big bang disruption. Harvard Business Review, 91 (3), 44–56.

Elsass, P. M. (1993). The paradox of success: Too much of a good thing? Academy of Management Perspectives, 7 (3), 84–85.

Etzioni, O., Lesh, N., & Segal, R. (1993). Building softbots for UNIX preliminary technical Report . University of Washington.

Evans, S., et al. (2017). Business model innovation for sustainability: Towards a unified perspective for creation of sustainable business models. Business Strategy and the Environment, 26 (5), 597–608.

Fartash, K., Davoudi, S. M. M., & Semnan, I. (2012). The important role of strategic agility in firms’ capability and performance. International Journal of Engineering and Management Research, 2 (3), 6–12.

Firstsource. (2019). RPA vs Cognitive Automation: Understanding the Difference. [Online] Available at: https://www.firstsource.com/rpa-vs-cognitive-automation-understanding-the-difference/ . [Accessed 25 April 2020]

Flyverbom, M., Deibert, R., & Matten, D. (2019). The governance of digital technology, big data, and the internet: New roles and responsibilities for business. Business & Society, 58 (1), 3–19.

Foss, N. J., & Saebi, T. (2018). Business models and business model innovation: Between wicked and paradigmatic problems. Long Range Planning, 51 (1), 9–21.

Gans, J. S., & Stern, S. (2003). The product market and the market for ‘ideas’: Commercialization strategies for technology entrepreneurs. Research Policy, 32 (2), 333–350.

Gauthier, C., Bastianutti, J., & Haggège, M. (2018). Managerial capabilities to address digital business models: The case of digital health. Strategic Change, 27 (2), 173–180.

Gebauer, H., Fleisch, E., Lamprecht, C., & Wortmann, F. (2020). Growth paths for overcoming the digitalization paradox. Business Horizons, 63 (3), 313–323.

Graham, S. J., & Mowery, D. C. (2006). The use of intellectual property in software: Implications for open innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), Open Innovation: Researching a New Paradigm (pp. 184–204). Oxford University Press on Demand.

Hamel, G. (2000). Leading the revolution . Harvard Business School Press.

Helfat, C. E., et al. (2007). Dynamic capabilities: Understanding strategic change in organizations . Blackwell Publishing.

Hodgson, G. M. (2003). Capitalism, complexity, and inequality. Journal of Economic Issues, 37 (2), 471–478.

Howe, J. (2008). Crowdsourcing: How the power of the crowd is driving the future of business . Business Books.

Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: How machine intelligence changes the rules of business. Harvard Business Review, 98 , 3–9.

IBA Global Employment Institute. (2017). Artificial intelligence and robotics and their impact on the workplace, s.l.: IBA Global Employment Institute.

Juniper Research. (2019). Bank Cost Savings via Chatbots to Reach $7.3 Billion by 2023, as Automated Customer Experience Evolves. [Online] Available at: https://www.juniperresearch.com/press/press-releases/bank-cost-savings-via-chatbots-reach-7-3bn-2023 . [Accessed 25 March 2020]

Kamalaldin, A., Linde, L., Sjödin, D., & Parida, V. (2020). Transforming provider-customer relationships in digital servitization: A relational view on digitalization. Industrial Marketing Management, 89 , 306–325.

Kelley, K. (2019). Address anonymity and data privacy in chatbot security. [Online] Available at: https://searchenterpriseai.techtarget.com/feature/Address-anonymity-and-data-privacy-in-chatbot-security . [Accessed 30 March 2020]

Kiat, O. K. (2017). 3 Advantages of Chatbots in CRM. [Online] Available at: https://chatbotsmagazine.com/3-advantages-of-chatbots-in-crm-3d4adbbba34d . [Accessed 21 April 2020]

Kim, I. H. S. (2021). The comprehensive unified paradigm for business model innovation. Technology Analysis & Strategic Management, pp. 1–22.

Kohtamäki, M., et al. (2019). Digital servitization business models in ecosystems: A theory of the firm. Journal of Business Research, 104 , 380–392.

LaMothe, E., & Bobek, D. (2020). Are individuals more willing to lie to a computer or a human? Evidence from a tax compliance setting. Journal of Business Ethics, 167 , 157–180.

Laursen, K., & Salter, A. J. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43 (5), 867–878.

Leavitt, H. J. (1964). Applied organization change in industry: Structural, technical and human approaches. In: W. W. C. &. H. J. L. M. W. Shelly, ed. New perspectives in organization research journal. John Wiley & Sons., p. 55–71.

Lewin, A. Y., & Volberda, H. W. (1999). Prolegomena on coevolution: A framework for research on strategy and new organizational forms. Organization Science, 10 (5), 519–534.

Lobschat, L., et al. (2021). Corporate digital responsibility. Journal of Business Research, 122 , 875–888.

Magretta, J. (2002). Why business models matter. Harvard Business Review, 86–92.

McAfee, A., et al. (2012). Big data: The management revolution. Harvard Business Review, 90 (10), 60–68.

McGrath, R. G. (2010). Business models: A discovery driven approach. Long Range Planning, 43 (2–3), 247–261.

McWilliam, S. E., Kim, J. K., Mudambi, R. & Nielsen., B. B. (2019). Global value chain governance: Intersections with international business. Journal of World Business, 1–18.

Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly, 28 (2), 283–322.

Miller, D. (1990). The Icarus Paradox: How Exceptional Companies Bring about Their Own Downfall . Harper Business.

MIT Technology Review. (2018). Humans + bots: Tension and opportunity. [Online] Available at: https://www.technologyreview.com/2018/11/14/239924/humans-bots-tension-and-opportunity/ . [Accessed 25 March 2020]

Mitchell, D., & Coles, C. (2003). The ultimate competitive advantage of continuing business model innovation. Journal of Business Strategy, 24 (5), 15–21.

Moazed, A. (2018). Why GE digital failed. [Online] Available at: https://incafrica.com/alex-moazed/why-ge-digital-didnt-make-it-big.html . [Accessed 5 April 2022]

Morris, M., Schindehutte, M., & Allen, J. (2005). The entrepreneur’s business model: Toward a unified perspective. Journal of Business Research, 58 (6), 726–735.

Mullainathan, S., & Obermeyer, Z. (2017). Does machine learning automate moral hazard and error? American Economic Review, 107 (5), 476–480.

Ng, I. C., & Wakenshaw, S. Y. (2017). The internet-of-things: Review and research directions. International Journal of Research in Marketing, 34 (1), 3–21.

Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22 (3), 336–359.

OECD. (2007). Summary Report of the Study on Globalisation and Innovation in the Business Services Sector . OECD.

Oliver, C. (1997). The influence of institutional and task environment relationships on organizational performance: The Canadian construction industry. Journal of Management Studies, 34 (1), 99–124.

Opresnik, D., & Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165 , 174–184.

Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers . John Wiley & Sons.

Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16 (1), 2–40.

Paiola, M., & Gebauer, H. (2020). Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing firms. Industrial Marketing Management, 89 , 245–264.

Parida, V., Sjödin, D., & Reim, W. (2019). Leveraging digitalization for advanced service business models: Reflections from a systematic literature review and special issue contributions. Sustainability, 11 (2), 391.

Parmar, R., Mackenzie, I., Cohn, D., & Gann, D. (2014). The new patterns of innovation. Harvard Business Review, 92 (1), 86–95.

Paschou, T., Rapaccini, M., Adrodegari, F., & Saccani, N. (2020). Digital servitization in manufacturing: A systematic literature review and research agenda. Industrial Marketing Management, 89 (8), 278–292.

Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69 (4), 167–176.

Powell, W. W., & Snellman, K. (2004). The knowledge economy. Annual Review of Sociology, 30 , 199–220.

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85 (1), 131–151.

Rabaya, A. J., & Saleh, N. M. (2022). The moderating effect of IR framework adoption on the relationship between environmental, social, and governance (ESG) disclosure and a firm’s competitive advantage. Environment, Development and Sustainability, 24 (2), 2037–2055.

Rogers, E. M. (1995). Diffusion of innovations (4th ed.). The Free Press.

Ross, J., et al. (2010). Who are the crowdworkers? Shifting demographics in Mechanical Turk . CHI.

Sanchez, P., & Ricart, J. E. (2010). Business model innovation and sources of value creation in low-income markets. European Management Review, 7 (3), 138–154.

Santos, F., Pereira, R., & Vasconcelos, J. B. (2020). Toward robotic process automation implementation: An end-to-end perspective. Business Process Management Journal, 26 (2), 405–420.

Santos, J., Spector, B., & Van der Heyden, L. (2015). Toward a theory of business model innovation within incumbent firms. In N. Foss & T. Saebi (Eds.), Model Innovation: The Organizational Dimension (pp. 43–63). Oxford University Press.

Chapter   Google Scholar  

Schryen, G. (2013). Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22 (2), 139–169.

Schwab, K. (2017). The Fourth Industrial Revolution . Crown Business.

Scott, W. R. (1987). The adolescence of institutional theory. Administrative Science Quarterly, 32 , 493–511.

Sewpersadh, N. S. (2019a). A theoretical and econometric evaluation of corporate governance and capital structure in JSE-listed companies. Corporate Governance The International Journal of Business in Society, 19 (5), 1063–1081.

Sewpersadh, N. S. (2019b). An examination of CEO power with board vigilance as a catalyst for firm growth in South Africa. Measuring Business Excellence, 23 (4), 377–395.

Siemens. (2020). Global Services. [Online] Available at: https://new.siemens.com/pt/en/company/about-siemens/shared-services.html . [Accessed 31 March 2020]

Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134 , 574–587.

SSON. (2018). Global Business Services: Transformation Driver & Digital Enabler . Shared Services & Outsourcing Network.

Stringfellow, A., Nie, W., & Bowen, D. E. (2004). CRM: Profiting from understanding customer needs. Business Horizons, 47 (5), 45–52.

Surowiecki, J. (2004). The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations . 1st Doubleday Books.

Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43 (2–3), 172–194.

Teece, D. J. (2014). A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. Journal of International Business Studies, 45 (1), 8–37.

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51 (1), 40–49.

Toffler, A. (1970). Future shock . Bantam books.

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14 (3), 207–222.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59 (236), 433–460.

van der Aalst, W. M., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60 (4), 269–272.

Velu, C., & Stiles, P. (2013). Managing decision-making and cannibalization for parallel business models. Long Range Planning, 46 (6), 443–458.

Vendrell-Herrero, F., Bustinza, O. F., Parry, G., & Georgantzis, N. (2017). Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60 , 69–81.

Visnjic, I., Jovanovic, M., Neely, A., & Engwall, M. (2017). What brings the value to outcome-based contract providers? Value drivers in outcome business models. International Journal of Production Economics, 192 , 169–181.

Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26 (2), xiii–xxiii.

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9 (1), 36–45.

Weinstein, A. T. (2020). Business models for the now economy. Journal of Business Strategy., 42 , 391.

Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52 , 546–553.

Willcocks, L. P., Lacity, M., & Craig, A. (2015). The IT function and robotic process automation . The Outsourcing Unit Working Research Paper Series.

World Trade Organization. (2019). Global Value Chain Development Report 2019: Technological Innovation, Supply Chain Trade, and Workers in a Globalized World . World Bank Group.

Yang, D. H., You, Y. Y., & Kwon, H. J. (2014). A framework for business model innovation using market, component and innovation tool. International Journal of Applied Engineering Research, 9 (21), 9235–9248.

Yuan, Y., & Wang, F.-Y. (2018). Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48 (9), 1421–1428.

Zuboff, S. (1988). In the age of the smart machine: The future of work and power . Basic books.

Zucker, L. G. (1987). Institutional theories of organization. Annual Review of Sociology, 13 (1), 443–464.

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Business Process Management Journal

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Article publication date: 12 May 2020

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The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.

Design/methodology/approach

This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.

AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.

Research limitations/implications

AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.

Originality/value

This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

• Artificial intelligence
• Cases studies
• Business value
• Process innovation
• Firm performance

Wamba-Taguimdje, S.-L. , Fosso Wamba, S. , Kala Kamdjoug, J.R. and Tchatchouang Wanko, C.E. (2020), "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects", Business Process Management Journal , Vol. 26 No. 7, pp. 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

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A Stakeholder Theory Perspective on Business Models: Value Creation for Sustainability

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• Published: 08 February 2019
• Volume 166 , pages 3–18, ( 2020 )

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• Birte Freudenreich 1 ,
• Florian Lüdeke-Freund 2 &
• Stefan Schaltegger 1  

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Business models are developed and managed to create value. While most business model frameworks envision value creation as a uni-directional flow between the focal business and its customers, this article presents a broader view based on a stringent application of stakeholder theory. It provides a stakeholder value creation framework derived from key characteristics of stakeholder theory. This article highlights mutual stakeholder relationships in which stakeholders are both recipients and (co-) creators of value in joint value creation processes. Key findings include that the concept and analysis of value creation through business models need to be expanded with regard to (i) different types of value created with and for different stakeholders and (ii) the resulting value portfolio, i.e., the different kinds of value exchanged between the company and its stakeholders. This paper details the application of the stakeholder value creation framework and its theoretical propositions for the case of business models for sustainability. The framework aims to support theoretical and empirical analyses of value creation as well as the management and transformation of business models in line with corporate sustainability ambitions and stakeholder expectations. Overall, this paper proposes a shift in perspective from business models as devices of sheer value creation to business models as devices that organize and facilitate stakeholder relationships and corresponding value exchanges.

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Abdelkafi, N., Makhotin, S., & Posselt, T. (2013). Business models innovations for electric mobility – What can be learned from existing business model patterns? International Journal of Innovation Management, 17 (1), 1–41.

Google Scholar  

Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19 (3), 359–376.

Andersson, B., Bergholtz, M., Edirisuriya, A., Ilayperuma, T., Johannesson, P., Gordijn, J., Grégoire, B., Schmitt, M., Dubois, E., & Abels, S. (2006). Towards a reference ontology for business models. In Conceptual Modeling-ER 2006 (pp. 482–496). New York: Springer.

Ballon, P. (2007). Business modelling revisited: The configuration of control and value. Journal of Policy, Regulation and Strategy for Telecommunications, Information and Media, 9 (5), 6–19.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17 (1), 99–120.

Bocken, N., Short, S., Rana, P., & Evans, S. (2013). A value mapping tool for sustainable business modelling. Corporate Governance: The International Journal of Business in Society, 13 (5), 482–497.

Bocken, N., Short, S., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65 , 42–56.

Boons, F., & Lüdeke-Freund, F. (2013). Business models for sustainable innovation: State of the art and steps towards a research agenda. Journal of Cleaner Production, 45 , 9–19.

Bouwman, H., & van Den Ham, E. (2003). Exploring value networks enabling the delivery of back office content to mobile workers. ITI’03 Europrix Conference. Tampere.

Bouwman, H., Zhengjia, M., van der Duin, P., & Limonard, S. (2008). A business model for IPTV service: A dynamic framework. Info , 10(3), 22–38.

Breuer, H., Fichter, K., Lüdeke-Freund, F., & Tiemann, I. (2018). Sustainability-oriented business model development: Principles, criteria, and tools. International Journal of Entrepreneurial Venturing, 10 (2), 256–286.

Breuer, H., & Lüdeke-Freund, F. (2017a). Values-based innovation management: Innovating by what we care about . Houndmills: Palgrave Macmillan.

Breuer, H., & Lüdeke-Freund, F. (2017b). Values-based network and business model innovation. International Journal of Innovation Management . https://doi.org/10.1142/S1363919617500281 .

Article   Google Scholar  

Bridoux, F., & Stoelhorst, J. W. (2016). Stakeholder relationships and social welfare: A behavioral theory of contributions to joint value creation. Academy of Management Review, 41 (2), 229–251.

Brickson, S. L. (2007). Organizational identity orientation: The genesis of the role of the firm and distinct forms of social value. Academy of Management Review, 32 (3), 864–888.

Busch, T., Bauer, R., & Orlitzky, M. (2016). Sustainable development and financial markets: Old paths and new avenues. Business & Society, 55 (3), 303–329.

Casadesus-Masanell, R., & Ricart, J. E. (2010). From strategy to business models and onto tactics. Long Range Planning, 43 (2–3), 195–215.

Chesbrough, H. (2007). Business model innovation: It’s not just about technology anymore. Strategy & Leadership, 35 (6), 12–17.

Christ, K. L., Burritt, R. L., Guthrie, J., & Evans, E. (2018). The potential for ‘boundary-spanning organisations’ in addressing the research-practice gap in sustainability accounting. Sustainability Accounting, Management and Policy Journal . https://www.emeraldinsight.com/doi/abs/10.1108/SAMPJ-06-2017-0059 . Accessed 02 Feb 2019.

Darnall, N., Henriques, I., & Sadorsky, P. (2010). Adopting proactive environmental strategy: The influence of stakeholders and firm size. Journal of Management Studies, 47 (6), 1072–1094.

Dentoni, D., Bitzer, V., & Pascucci, S. (2016). Cross-sector partnerships and the co-creation of dynamic capabilities for stakeholder orientation. Journal of Business Ethics, 135 (1), 35–53.

Donaldson, T., & Preston, L. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. The Academy of Management Review, 20 (1), 65–91.

Dreyer, B., Lüdeke-Freund, F., Hamann, R., & Faccer, K. (2017). Upsides and downsides of the sharing economy: Collaborative consumption business models’ stakeholder value impacts and their relationship to context. Technological Forecasting and Social Change, 125 , 87–104.

Elkington, J. (2004). Enter the triple bottom line. In J. Elkington (Ed.), The triple bottom line: Does it all add up? (pp. 1–12). London: Earthscan.

Figge, F., & Schaltegger, S. (2000). What is “stakeholder value”? Developing a catchphrase into a benchmarking tool. Lueneburg: Universiy of Lueneburg.

Freeman, E. (1984). Stakeholder management: Framework and philosophy . Mansfield: Pitman.

Freeman, E. (2010). Managing for stakeholders: Trade-offs or value creation. Journal of Business Ethics, 96 , 7–9.

Freeman, R. E., Pierce, J., & Dodd, R. H. (2000). Environmentalism and the new logic of business: How firms can be profitable and leave our children a living planet . Oxford: Oxford University Press.

Garriga, E. (2014). Beyond stakeholder utility function: Stakeholder capability in the value creation process. Journal of Business Ethics, 120 (4), 489–507.

Gordijn, J., & Akkermans, H. (2001). Designing and evaluating e-business models. IEEE Intelligent Systems, 16 (4), 11–17.

Gordijn, J., Akkermans, H., & van Vliet, H. (2000). What’s in an electronic business model? In: International Conference on Knowledge Engineering and Knowledge Management. Springer Berlin Heidelberg, 257–273.

Haigh, N., & Hoffmann, A. (2014). The new heretics: Hybrid organizations and the challenges they present to corporate sustainability. Organization & Environment, 27 (3), 223–241.

Haslam, C., Tsitsianis, N., Andersson, T., & Gleadle, P. (2015). Accounting for business models: Increasing the visibility of stakeholders. Journal of Business Models, 3 (1), 62–80.

Hörisch, J., Freeman, E., & Schaltegger, S. (2014). Applying stakeholder theory in sustainability management: Links, similarities, and a conceptual framework. Organization & Environment, 27 (4), 1–19.

Johnson, M. W. (2010). Seizing the white space: Business model innovation for growth and renewal . Boston: Harvard Business Press.

Joyce, A., & Paquin, R. (2016). The triple layered business model canvas: A tool to design more sustainable business models. Journal of Cleaner Production, 135 , 1474–1486.

Kurucz, E., Colbert, B., Lüdeke-Freund, F., Upward, A., & Willard, B. (2017). Relational leadership for strategic sustainability: Practices and capabilities to advance the design and assessment of sustainable business models. Journal of Cleaner Production, 140 , 189–204.

Lambert, S. (2012). Deconstructing business model frameworks using a reference model. University of South Australia. Adelaide, Occasional Working Paper No.4.

Lee, M.-D. (2011). Configuration of external influences: The combined effects of institutions and stakeholders on corporate social responsibility strategies. Journal of Business Ethics, 102 (2), 281–298.

Lüdeke-Freund, F., & Dembek, K. (2017). Sustainable business model research and practice: Emerging field or passing fancy? Journal of Cleaner Production, 168 , 1668–1678.

Lüdeke-Freund, F., Freudenreich, B., Saviuc, I., Schaltegger, S., & Stock, M. (2017). Sustainability-oriented business model assessment: A conceptual foundation. In E. Carayannis & S. Sindakis (Eds.), Analytics, innovation and excellence-driven enterprise sustainability (pp. 169–206). Houndmills: Palgrave.

Lüdeke-Freund, F., Massa, L., Bocken, N., Brent, A., & Musango, J. (2016). Business models for shared value: Main report . Cape Town: Network for Business Sustainability South Africa.

Marine Stewardship Council MSC (2017). The MSC in numbers. Retrieved June 7, 2017 from https://www.msc.org/global-impacts/key-facts-about-msc?set_language=en .

Massa, L., Tucci, C. L., & Afuah, A. (2017). A critical assessment of business model research. Academy of Management Annals, 11 (1), 73–104.

Max-Neef, M. A., Elizalde, A., & Hopenhayn, M. (1991). Human scale development: Conception, application and further reflections . New York: The Apex Press.

Miller, T. R., Wiek, A., Sarewitz, D., Robinson, J., Olsson, L., Kriebel, D., & Loorbach, D. (2014). The future of sustainability science: A solutions-oriented research agenda. Sustainability Science, 9 (2), 239–246.

Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers . Hoboken: Wiley.

Osterwalder, A., Pigneur, Y., & Tucci, C. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16 (1), 1–25.

Parmar, B. L., Freeman, E., Harrison, J. S., Wicks, A. C., & de Colle, S. (2010). Stakeholder theory: The state of the art . Cambridge: Cambridge University Press.

Patala, S., Jalkala, A., Keränen, J., Väisänen, S., Tuominen, V., & Soukka, R. (2016). Sustainable value propositions: Framework and implications for technology suppliers. Industrial Marketing Management, 59 , 144–156.

Payne, A., Storbacka, K., & Frow, P. (2008). Managing the co-creation of value. Journal of the Academy of Marketing Science, 36 (1), 83–96.

Pedersen, E. R. G., Gwozdz, W., & Kant Hvass, K. (2018). Exploring the relationship between business model innovation, corporate sustainability, and organisational values within the fashion industry. Journal of Business Ethics, 149 (2), 267–284.

Rauter, R., Perl-Vorbach, E., & Baumgartner, R. (2017). Is open innovation supporting sustainable innovation? Findings based on a systematic, explorative analysis of existing literature. International Journal of Innovation and Sustainable Development, 11 (2/3), 249–270.

Richardson, J. (2008). The business model: An integrative framework for strategy. Strategic Change, 17 (5–6), 133–144.

Schaltegger, S., & Figge, F. (2000). Environmental shareholder value: economic success with corporate environmental management. Eco-Management and Auditing: The Journal of Corporate Environmental Management , 7 (1), 29–42.

Schaltegger, S., Hansen, E. G., & Lüdeke-Freund, F. (2016a). Business models for sustainability: Origins, present research, and future avenues. Organization & Environment, 29 (1), 3–10.

Schaltegger, S., Hörisch, J., & Freeman, R. E. (2017). Business cases for sustainability: a stakeholder theory perspective. Organization & Environment . https://doi.org/10.1177/1086026617722882 .

Schaltegger, S., Lüdeke-Freund, F., & Hansen, E. (2012). Business cases for sustainability. The role of business model innovation for corporate sustainability. International Journal of Innovation and Sustainable Development, 6 (2), 95–119.

Schaltegger, S., Lüdeke-Freund, F., & Hansen, E. G. (2016b). Business models for sustainability: A co-evolutionary analysis of sustainable entrepreneurship, innovation, and transformation. Organization & Environment, 29 (3), 264–289.

Schneider, T., & Sachs, S. (2017). The impact of stakeholder identities on value creation in issue-based stakeholder networks. Journal of Business Ethics, 144 (1), 41–57.

Shafer, S. M., Smith, H. J., & Linder, J. C. (2005). The power of business models. Business Horizons, 48 (3), 199–207.

Sommer, A. (2012). Managing green business model transformations . New York: Springer.

Spitzeck, H., & Hansen, E. G. (2010). Stakeholder governance: How stakeholders influence corporate decision making. Corporate Governance: The International Journal of Business in Society, 10 (4), 378–391.

Stubbs, W., & Cocklin, C. (2008). Conceptualizing a “sustainability business model”. Organization & Environment, 21 (2), 103–127.

Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43 (2/3), 172–194.

Timmers, P. (1998). Business models for electronic markets. Electronic Markets, 8 (2), 3–8.

Tukker, A. (2015). Product services for a resource-efficient and circular economy – a review. Journal of Cleaner Production, 97 , 76–91.

Upward, A., & Jones, P. (2016). An ontology for strongly sustainable business models: Defining an enterprise framework compatible with natural and social science. Organization & Environment, 29 (1), 97–123.

Wirtz, B. W., Pistoia, A., Ullrich, S., & Göttel, V. (2016). Business models: Origin, development and future research perspectives. Long Range Planning, 49 (1), 36–54.

Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25 (2), 139.

Yang, M., Evans, S., Vladimirova, D., & Rana, P. (2017). Value uncaptured perspective for sustainable business model innovation. Journal of Cleaner Production, 140 , 1794–1804.

Zott, C., & Amit, R. (2010). Business model design: An activity system perspective. Long Range Planning, 43 (2–3), 216–226.

Zott, C., Amit, R., & Massa, L. (2011). The business model: Recent developments and future research. Journal of Management, 37 (4), 1019–1042.

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Acknowledgements

The authors would like to thank Iolanda Saviuc for her support in an early stage of developing the framework.

Authors Birte Freudenreich and Stefan Schaltegger have received a research Grant (No 01UT1425D) from the German Ministry of Science and Education (BMBF).

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Freudenreich, B., Lüdeke-Freund, F. & Schaltegger, S. A Stakeholder Theory Perspective on Business Models: Value Creation for Sustainability. J Bus Ethics 166 , 3–18 (2020). https://doi.org/10.1007/s10551-019-04112-z

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Theses and Dissertations in Business Administration

Theses and dissertations published by graduate students in the Business Administration program, College of Business, Old Dominion University, since Fall 2016 are available in this collection. Backfiles of all dissertations (and some theses) have also been added.

In late Fall 2023 or Spring 2024, all theses will be digitized and available here. In the meantime, consult the Library Catalog to find older items in print.

Theses/Dissertations from 2023 2023

Dissertation: Two Essays on Industry Tournament Incentives , Sarah Almisher

Dissertation: Two Essays on Investor Sentiment , Amin Amoulashkarian

Dissertation: Two Essays on Retail Trading , Qiqi Liang

Dissertation: Two Essays in Real Estate Dynamics , Navid Safari

Dissertation: Firm Capabilities, Great Power Competition, and the Structural Reshaping of Globalization , Samuel Wilson

Theses/Dissertations from 2022 2022

Dissertation: Three Essays on Stock Price Informativeness, Stock Price Momentum, and Firm Investment Efficiency , Chen Chen

Dissertation: Exploring Blockchain-Based Digital Transformation In Organizations , Weiru Chen

Dissertation: Two Essays on Antecedents and Effects of Award-Winning CEOS , Veronika Ciarleglio

Dissertation: Two’s a Crowd? Implications of Economic Geography for Corporate Governance , Matthew Farrell

Dissertation: Two Essays on the Effects of CEO Social Activism , Habib Islam

Dissertation: Two Essays on the Role of Empathy in Consumer Response to User-Generated Content , Mohammadali Koorank Beheshti

Dissertation: Three Essays on the Effects of Other Customer Brand Tie and Employee Behavior on Consumer Behavior , Saeed Zal

Dissertation: Three Essays on CEO Traits, Corporate Investment Decisions, and Firm Value , Rongyao Zhang

Theses/Dissertations from 2021 2021

Dissertation: Two Essays on Antecedents and Effects of Board Female Representation Non-Conformity , Fatemeh Askarzadeh

Dissertation: Application of Optimization Techniques in Corporate Cash Management , Venkateswara Reddy Dondeti

Dissertation: Two Essays on Corruption, FDI, and Digitalization , Mahdi Forghani Bajestani

Dissertation: Two Essays on the Information Embedded in Flow of Exchange-Traded Funds (ETFs) , Hamed Yousefi

Theses/Dissertations from 2020 2020

Dissertation: The Influence of Mating Motives on Reliance on Form Versus Function in Product Choice , Seyed Hamid Abbassi Hosseini

Dissertation: Three Essays on CEO Characteristics and Corporate Bankruptcy , Rajib Chowdhury

Dissertation: The Effects of CEO Dismissal Risk and Skills on Risky Corporate Decisions and CEO Compensation , Son T. Dang

Dissertation: Essay 1: How We Feel: The Role of Macro-Economic Sentiment in Advertising Spending-Sales Relationship; Essay 2: It Was the Best of Times; It Was the Worst of Times: The Effect of Emotional Uncertainty and Arousal on Healthy Food Choices , Leila Khoshghadam

Dissertation: The Accumulation of IT Capability And Its Long-Term Effect on Financial Performance , Jin Ho Kim

Dissertation: Three Essays on the Roles of Review Valence and Conflict in Online Relationships , Ran Liu

Dissertation: Two Essays on the Microstructure of the Housing Market: Agents' Diffused Effort and Sellers' Behavior Bias , Zhaohui Li

Dissertation: Two Essays on CEO Overconfidence in Relation to Speed of Adjustment of Firm Financial Policy and CEO Inside Debt , Xiang Long

Dissertation: Pricing the Cloud: An Auction Approach , Yang Lu

Dissertation: Two Essays on Consumer Envy , Murong Miao

Dissertation: Two Essays on Negotiations Between Entrepreneurs and Angel Investors , Aydin Selim Oksoy

Theses/Dissertations from 2019 2019

Dissertation: Two Essays on Bitcoin Price and Volume , Mohammad Bayani Khaknejad

Dissertation: Two Essays on Investor Attention, Investor Sentiment, and Earnings Pricing , Qiuye Cai

Dissertation: Success Factors Impacting Artificial Intelligence Adoption --- Perspective From the Telecom Industry in China , Hong Chen

Dissertation: Early Information Access to Alleviate Emergency Department Congestion , Anjee Gorkhali

Dissertation: Two Essays on the Consumer Acculturation Process – A Need for and Development of a Consumer Acculturation Measure , Kristina Marie Harrison

Dissertation: Three Essays on CEO Characteristics and Corporate Decisions , Trung Nguyen

Dissertation: Two Essays on the Effects of Organization Capital on Firm Behavior , Andrew Root

Dissertation: Underlying Factors Behind Generation of Different Types of User-Generated Content - Impact of Individual and Brand/Product Level Factors in Generation of Brand-Oriented Content and Community-Oriented Content , Kemal Cem Soylemez

Dissertation: Customers’ Goal-Related Behavior in Loyalty Programs , Junzhou Zhang

Theses/Dissertations from 2018 2018

Dissertation: Security Risk Tolerance in Mobile Payment: A Trade-off Framework , Yong Chen

Dissertation: Numerical Framing and Emotional Arousal as Moderators of Review Valence and Consumer Choices , Anh Dang

Dissertation: Three Essays on CEO Risk Preferences, and Ability, Corporate Hedging Decisions, and Investor Sentiment , Sonik Mandal

Dissertation: Two Essays on the Creation and Success of New Ventures , Amirmahmood Amini Sedeh

Dissertation: Effectiveness of Social Media Analytics on Detecting Service Quality Metrics in the U.S. Airline Industry , Xin Tian

Dissertation: Two Essays on Value Co-Creation , Hangjun Xu

Theses/Dissertations from 2017 2017

Dissertation: Two Essays on Forced CEO Turnover During Envy Merger Waves, and Dividends , Bader Almuhtadi

Dissertation: The Role of Consumer Ethnocentrism on the Effects of Domestic vs Foreign Product Failure on Post Consumption Emotions and Complaint Behaviors , Kittinand Bandhumasuta

Dissertation: The Impact of Help-Self and Help-Others Appeals Upon Participation in Clinical Research Trials , Susan Lewis Casey

Dissertation: Is Every Tweet Created Equal? A Framework to Identify Relevant Tweets for Business Research , Thad Chee

Dissertation: Three Essays on Mutual Funds, Fund Management Skills, and Investor Sentiment , Feng Dong

Dissertation: Two Essays on the Impact of Institutional Structures on Entrepreneurship: Country Level Analysis , Mehdi Sharifi Khobdeh

Dissertation: Two Essays on the Antecedents and Effects of Internationalizing Out of Emerging and Developed Economies , Mark Robert Mallon

Dissertation: From Placebo to Panacea: Exploring the Influence of Price, Suspicion, and Persuasion Knowledge on Consumers’ Perception of Quality , Vahid Rahmani

Dissertation: Essays on the El Niño Anomaly and Stock Return Predictability , Zhijun Yang

Theses/Dissertations from 2016 2016

Dissertation: The Effect of XBRL and Social Media on Information Asymmetry: Evidence from Bank Loan Contracts , Dazhi Chong

Dissertation: Two Essays on CEO Inside Debt Holding in Relation to Firm Payout Policy and Financial Reporting , Asligul Erkan

Dissertation: Two Essays on The Internationalization Speed of New Ventures , Orhun Guldiken

Dissertation: Two Essays on Shareholder Base, Firm Behavior, and Firm Value , Yi Jian

Dissertation: Valence or Volume? Maximizing Online Review Influence Across Consumers, Products, and Marketing , Elika Kordrostami

Dissertation: Essays on the Equity Risk Premium , Mohamed Mehdi Rahoui

Dissertation: A Study of the Impact of Information Blackouts on the Bullwhip Effect of a Supply Chain Using Discrete-Event Simulations , Elizabeth Rasnick

Dissertation: Two Essays on Investor Emotions and Their Effects in Financial Markets , Jiancheng Shen

Dissertation: Two Studies on The Use of Information Technology in Collaborative Planning, Forecasting & Replenishment (CPFR) , David McCaw Simmonds

Dissertation: Founder CEOs and Initial Public Offerings: The Role of Narratives, Institutions and Cultural Context , Christina Helen Tupper

Dissertation: Ambidexterity: The Interplay of Supply Chain Management Competencies and Enterprise Resource Planning Systems on Organizational Performance , Serdar Turedi

Dissertation: Two Essays on Short Selling , Zhaobo Zhu

Dissertation: Buying Love Through Social Media: How Different Types Of Incentives Impact Consumers’ Online Sharing Behavior , Yueming Zou

Theses/Dissertations from 2015 2015

Dissertation: Three Essays on Dividend Policy , Mehmet Deren Caliskan

Dissertation: "The Magic Formula: Scent and Brand"- The Influence of Olfactory Sensory Co-Branding on Consumer Evaluations and Experiences , Ceren Ekebas

Dissertation: The Value of Integrated Information Systems for U.S. General Hospitals , Liuliu Fu

Dissertation: Two Essays on Managerial Horizon, Cash Holdings and Earnings Management , Sanjib Guha

Dissertation: Three Essays on Opportunistic Claiming Behavior in a Services Setting: Customers and Front Line Employees Perspectives , Denis Khantimirov

Dissertation: Spillover Effects of Brand Alliance and Service Experience on Host Brands in Loyalty Program Partnerships , Gulfem Cigdem Kutlu

Dissertation: Measuring Consumer Expectations of Salesperson Unethicality: A Scale Development , Amiee Mellon

Dissertation: Essays on International Risk-Return Trade-Off Relations , Liang Meng

Dissertation: Two Essays on Investor Attention and Asset Pricing , Nadia Asmaa Nafar

Dissertation: International Venture Capital Firms Syndication and Performance: A Social Network Perspective , Amir Pezeshkan

Dissertation: Three Essays on Institutions, Entrepreneurship, and Development , Adam Smith

Theses/Dissertations from 2014 2014

Dissertation: An Empirical Examination of the Antecedents and Consequences of Earnings Management in Emerging Markets , Shuji Rosey Bao

Dissertation: Dynamic Capabilities and Resilient Organizations Amid Environmental Jolts , Stav Fainshmidt

Dissertation: An Empirical Examination of the Moderators of Direct Versus Indirect Comparative Advertising , Chun-Kai Hsu

Dissertation: Two Essays on Attracting Foreign Direct Investment: From Both a National and Firm Level Perspective , Ryan Lawrence Mason

Dissertation: The Effect of Online Reviews on Attitude and Purchase Intention: How Consumers Respond to Mixed Reviews , Chatdanai Pongpatipat

Dissertation: Three Essays on the Enterprise Strategy for Multinational Firms , Veselina Plamenova Vracheva

Dissertation: The Antecedents and Effects of Strategic Caring: A Cross-National Empirical Study , Thomas Weber

Theses/Dissertations from 2013 2013

Dissertation: International Banking sector Linkages: Did the Global Financial Crisis Strengthen or Weaken the Linkages? , James Edward Benton

Dissertation: Three Essays on Corporate Liquidity, Financial Crisis, and Real Estate , Kimberly Fowler Luchtenberg

Dissertation: Three Essays on Immigrant Entrepreneurship , Kaveh Moghaddam

Dissertation: The Response of Commercial Banks to Credit Stimuli , Denise Williams Streeter

Theses/Dissertations from 2012 2012

Dissertation: An Examination of Middle Manager Innovation Behaviors and Institutional Factors Impact on Organizational Innovation in the USA and Mexico , J. Lee Brown III

Dissertation: Essays on Foreign Reverse Mergers and Bond ETF Mispricing , Charles William Duval

Dissertation: Three Essays on Strategic Risk Taking , Krista Burrill Lewellyn

Dissertation: Two Essays on Executive Pay and Firm Performance , Thuong Quang Nguyen

Dissertation: A Study of Risk-Taking Behavior in Investment Banking , Elzotbek Rustambekov

Dissertation: A Study of Failures in the US Banking Industry , Joseph Trendowski

Dissertation: Two Essays on Behavioral Finance , Quang Viet Vu

Theses/Dissertations from 2011 2011

Dissertation: Three Essays on Individual Currency Traders , Boris Sebastian Abbey

Dissertation: Cross-listing Premium or Market Timing , Moustafa M. Abu El Fadl

Dissertation: Warranty and Price as Quality Signals: The effect of Signal Consistency and Unexpectedness on Product Perception , Sultan Alaswad Alenazi

Dissertation: The Behavior and Choices of Serial Bidders in M&A Transactions: A Prospect Theory Approach , Ahmed Essam El-Din El-Bakry

Dissertation: Two Essays on the Effect of Macroeconomic News on the Stock Market , Ajay Kongera

Dissertation: Intercultural Accommodation of Ethnic Minority Consumers: An Empirical Examination of the Moderating Effects in Service Encounters , Sarah Mady

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Dissertations / Theses on the topic 'Company business valuation'

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Mičudová, Soňa. "Business Valuation of the company Le Montmartre." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-113700.

Morys, Thomas. "Company valuation and environmental value." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/15046.

Levin, Joakim. "Essays in company valuation." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-660.

Olsson, Per. "Studies in company valuation." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://www.hhs.se/efi/summary/494.htm.

Petrikovič, Jan. "Valuation of Contractual Health Transportation Company." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-149961.

Jonsson, Emma, and Linda Samuelsson. "Business Valuation : Valuation of IT-companies in the area of Jönköping." Thesis, Jönköping University, JIBS, Business Administration, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-1322.

Background:

In Sweden Internet was introduced in 1983 and IT became a popular phe-nomenon in the 1990s. In the middle of this decade IT-companies had a prosperous period. Many companies acquired competitors frequently dur-ing these years in order to build brand names and stay competitive. More than 400 IT-companies went bankrupt during 2001, due to the burst of the IT-bubble. Today, there is no doubt that IT-companies are willing to acquire other companies in the industry. Before an acquisition both the purchaser and seller do a careful valuation of the current company, using different valuation methods. Lately, there are some IT-companies in the area of Jönköping and its surroundings that have carried out acquisitions.

In this thesis IT-companies in the area of Jönköping are considered in or-der to describe what valuation methods that are used when valuing these before an acquisition. Intangible assets are of great importance for this in-dustry. Therefore the aim is also to find out which these are and how they are valued.

In order to fulfill the purpose a qualitative research is maintained. Primary data is collected from two telephone interviews and six face-to-face inter-views. Three of the interviews are conducted with people working at IT-companies that have carried out an acquisition between 2006 and 2008. The other interviews were performed with people working with business valuation on a daily basis.

Conclusion:

When valuing IT-companies as well as the intangible assets, where good-will is significant due to synergies, the net present value approach is most commonly used. The relative valuation approach is also useful, especially for companies in the early phase of the life cycle since these do not show any historical facts. Within the IT-industry; P/S, P/E, and value per em-ployee, are all useful. The net asset value approach is the most common before a direct acquisition. In this research indirect acquisitions are most often applied.

I Sverige introducerades internet 1983 och IT blev ett populärt fenomen under 1990-talet. I mitten av decenniet hade IT-företagen en blomstrande period. Många företag förvärvade konkurrenter ofta för att skapa varu-märke och fortsätta vara konkurrenskraftiga. Över 400 IT-företag gick i konkurs under 2001 på grund av IT-bubblan. Idag är det ingen tvekan om att IT-företag är villiga att förvärva andra företag i denna industri. Innan ett förvärv genomför både förvärvaren och säljaren en noggrann värdering av det aktuella företaget med användning av olika värderingsmetoder. Det finns några IT-företag i Jönköpingsregionen som genomfört företagsför-värv på sista tiden.

Syftet i denna uppsats är att beskriva vilka värderingsmetoder IT-företag i Jönköpingsregionen använder vid värdering innan ett företagsförvärv. Immateriella tillgångar är viktiga i denna industri. Därför är syftet även att identifiera dessa och se hur de värderas.

För att uppfylla syftet används en kvalitativ metod. Primärdata är insamlad från två telefonintervjuer och de andra sex på intervjuobjektens kontor. Tre intervjuer genomfördes med personer som arbetar på IT-företag som genomfört företagsförvärv mellan 2006 och 2008. De andra intervjuerna genomfördes med personer som arbetar med företagsvärdering dagligen.

Vid värdering av IT-företag såväl som de immateriella tillgångarna, främst goodwill tack vare synergier, används i första hand avkastningsvärdering. Relativvärdering är också användbar, särskilt för företag i det tidiga skedet av livscykeln då ingen historisk information finns att tillgå. Inom IT-industrin är; P/S, P/E och värde per anställd, alla användbara. Substans-värdering är vanligast vid ett direkt förvärv. I denna studie är indirekta förvärv oftast förekommande.

Olsson, Fredrik, and Martin Persson. "Business Valuation : How to Value Private Limited Knowledge Based Companies." Thesis, Jönköping University, JIBS, Business Administration, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-9301.

Purpose The purpose of this study is to investigate the methods used for valuating private limited knowledge based companies and if a new approach is required, create or modify a foundation that will constitute as a base within the valuation process.

Method This is a qualitative study using interviews to obtain primary data. People working in the valuation industry were contacted and we got eight respondents. The questions were designed to answer our purpose and research questions. Telephone interviews were chosen due to the fact that we believed the response would be higher.     

Frame of References The theories used in this section is divided into three parts; the financial analysis including traditional valuating methods such as the Discounted Cash Flow model and relative valuating and multiples. The non-financial analysis focus on the underlying analysis consistent of structural- and intellectual capital and also value drivers that are creating value for the firm. In the end other theories concerning the analysis are presented, such as the risk-return trade-off, risk rating systems and analytical hierarchy process.            

Empirical Findings In this section the presentations of the respondents’ answers and

and Analysis a brief analysis related to each question. After this an extended analysis is presented focusing on the subject and our risk scheme and guidelines we created/modified. The extended analysis is connected to the respondents’ answers. The purpose of this section is to have a better understanding about the risk of transient intellectual capital and give recommendations how to handle it. Also, guidelines of how to weight different value driver are discussed.

Conclusion We concluded that all valuations utilize more than one approach in order to estimate the most accurate value for the company. For knowledge based companies the biggest risk with a M&A transaction is the probability of diminishing the intellectual capital. We constructed a model that will manage this risk based on our interviews and established theories.

Rosenblad, Mikael, André Weich, and Claes Wångehag. "The Family Business on the SSE : Family Ownership's Impact on a Valuation Process." Thesis, Jönköping University, JIBS, EMM (Entrepreneurship, Marketing, Management), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-735.

The main purpose of this thesis is to investigate the differences between family and non-family businesses that are listed on the stock exchange, more specifically which factors that is being used in the valuation process and why family businesses as a rule seem to be undervalued. We also look at if family ownership is a factor in this process.

By conducting interviews with analysts and journalists working with valuation we hope to be able to not only find out what factors differ but also why family busi-nesses are undervalued.

Our conclusion is that while the two forms of ownership has several negative factors that differ between them that are more common among family businesses, such as conservative dividend policy, this is not connected to the family business as a form but is rather an individual factor differing from company to company. Family ownership as such was however not in any way a factor in the valuation since the valuations instead looks at the individual company and does not generalize.

Velebová, Štěpánka. "Valuation of the company Gerresheimer Horsovsky Tyn spol. s r.o." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-124952.

De, Villiers Dirk Christiaan. "Determining the value of a new company with specific reference to the real option pricing theory." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52759.

Cabalzar, Filipi. "Business Plan, financial and risk analysis from the start-up mathrix." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/15856.

Lev, Martin. "Stanovení hodnoty firmy SOME Jindřichův Hradec, s. r. o." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-194129.

Cassone, Deandra Tillman. "A process to estimate the value of a company based on operational performance metrics." Diss., Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/66.

Benedicks, Anne, and Veronica Öberg. "Värderarens val av metod : Påverkansfaktorer vid företagsvärdering." Thesis, Södertörn University College, School of Business Studies, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-1156.

Title: The appraiser’s choice of valuation method – factors that influences the choice of company valuation methods

Seminar date: 04/06/07

Course: Master thesis in Business Administration, 10 Swedish credits.

Authors: Anne Benedicks and Veronica Öberg

Advisor: Eron Oxing

Profession of category: Financial analysts, auditors and company lawyers.

Key words: Company valuation, valuation methods, cash flow analysis, comparative valuation, the net asset value method.

The Main Issue: What is of decisive importance when choosing a special company valuation method?

Purpose: The purpose of this paper is to identify, analyse and evaluate the most common methods of valuation for financial analysts, auditors and company lawyers and those factors that influences the choice of method.

Method: A multiple survey has been implemented for the actual profession categories. Primary data was collected through semi-structured interviews and a questionnaire survey.

Theoretical: The theoretical frame of reference is based upon the paper’s dependent variable, i.e. the role of the appraiser. The appraiser is dependent of following undependent variables: comparative valuation of company, situations of valuation, relevant information, the processes of valuation, the methods of valuation and common custom valuation.

Empiricism: Material from the interviews and the questionnaire survey shows how the professional category respectively acts when they valuate a company and why and which valuation method is used. According to the interviews the financial analysts often uses comparative evaluation, the auditor uses the cash flow analysis while the lawyer chooses the net asset value method.

Conclusion: The result of this paper considerable agrees to earlier research within the field. The main underlying factor for the appraiser to choose a certain evaluation method is simply depending on the actual situation. Example of other determining factors is customer relations, access to relevant information and which type of business under evaluation.

Yrkesgrupper: Finansanalytiker, revisorer och jurister.

Problemformulering: Vad är avgörande för att en specifik företagsvärderingsmetod väljs?

Syfte: Syftet med uppsatsen är att identifiera, analysera och utvärdera vilka de vanligast förekommande värderingsmetoderna är för finansanalytiker, revisorer och jurister samt vilka faktorer som påverkar valet av metod.

Metod: En flerfallstudie har gjorts hos de berörda yrkeskategorierna. Primärdata samlades in genom semistrukturerade intervjuer samt enkätundersökning.

Teoretisk referensram: Den teoretiska referensramen utgår från uppsatsens beroende variabel, värderarens roll. De oberoende variabler som värderaren är beroende av är: relativ företagsvärdering, värderingssituationer, informationskällor, värderingsprocessen, värderingsmetoder och god värderingssed.

Empiri: Material från intervjuer och enkätundersökning visar på hur respektive yrkeskategori handlar i en företagsvärderingssituation samt varför och med vilken värderingsmetod. Enligt intervjuerna använder finansanalytikern oftast jämförande värdering, revisorn väljer kassaflödesanalys medan juristen väljer substansvärdemetoden.

Slutsats: Uppsatsens resultat överensstämmer på ett bra sätt med tidigare forskning inom området. Den största bakomliggande faktorn till att värderaren väljer en viss värderingsmetod beror oftast på situationen. Exempel på andra avgörande element är relationen till kunden, tillgång till information samt vilken typ av företag som ska värderas.

Gupta, Mayank. " “What are the different obstacles involved with the implementation of Real Options Valuation technique?” : A case study conducted in company X in Sweden." Thesis, Umeå universitet, Handelshögskolan vid Umeå universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-23095.

Pohořský, Jan. "Ocenění praxe praktického lékaře v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199242.

Adielsson, Magnus, Salmén Robin Harlos, and Robert Svensson. "Lagervärdering och lagerstyrning hos ett litet handelsföretag." Thesis, Mälardalen University, School of Sustainable Development of Society and Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-8296.

I ett litet företag finns en risk att ledningen fokuserar så mycket på kärnverksamheten att övriga administrativa verksamheter åsidosätts. Exempelvis finns det risk att företaget inte har full kontroll på sina lager.

Syftet med den här uppsatsen är att beskriva och analysera värderingen och styrningen av lagret hos ett litet handelsföretag med butik och i förekommande fall ge förbättringsförslag.

Vi har dels studerat lagar och rekommendationer avseende hantering av lager samt litteratur inom området lagerstyrning i handelsföretag. Dessutom har vi intervjuat företrädare för ett typiskt litet handelsföretag.

Det undersökta företaget uppfyller gällande lagar och rekommendationer, men inte så mycket mer. Vidare använder sig företaget mestadels av känsla och erfarenhet i sin lagerstyrning. Förtaget använder alltså inte formella analyser och metoder i någon större utsträckning, om ens någon. Vi har några förslag på förbättringar i detta avseende.

In a small company there is a risk that the management focuses so much on the core business that other administrative tasks are ignored. For example, there is a risk that the company does not control its inventories efficiently.

The purpose of this thesis is to describe and analyze the valuing and control of the inventory in a small retail company with a store and, where appropriate, give suggestions for improvement.

We have studied laws and recommendations regarding inventory valuation and other literature regarding inventory control in retail companies. Additionally we have interviewed representatives for a typical small retail company.

The investigated company does comply with applicable laws and recommendations, but not much more. Furthermore, the company uses mostly feel and experience in its inventory control. The company does not use formal inventory control in any great extent, if any. We have some suggestions for improvements in this regard.

Dani, Mercedesz, and Johanna Sterner. "Management & Valuation of Intangible Assets in Swedish Holding Companies : An integrative model on how Swedish holding companies assess, evaluate and manage their intangible assets to maintain old and create new knowledge within their subsidiaries." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Företagsekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-36557.

Kaving, Tomas, and Mathias Loogna. "En fallstudie i företagsvärdering." Thesis, Södertörn University College, School of Business Studies, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-1111.

När en värdering av ett företag skall göras finns det flera olika typer av värderingsmetoder som kan användas. Bakgrunden till den här studien är att uppsatsförfattarna blev kontaktade av ägarna till ett företag som undrade vad deras företag skulle vara värt vid en eventuell försäljning. Det specifika med företaget är att det endast arbetar mot en kund, samt att företaget nästan inte har några materiella tillgångar.

Syfte: Syftet är att kartlägga de olika värderingsmodeller som används vid värdering av företag, för att därefter klargöra vilken eller vilka metoder som är bäst lämpade för vårt fallföretag. Detta syftar till att resultera i en värdering av vårt fallföretag.

Metod: Vi har använt oss av en kvalitativ metod i form av en grundlig litteraturstudie, samt en genomgång av tidigare forskning. Vidare har ett antal e-postintervjuer genomförts och slutligen presenteras en modell för värdering av vårt fallföretag.

Teori: Den teoretiska delen av denna studie består av de värderingsmetoder som beskrivs i den litteratur som finns inom området. Vidare redovisas en del teori i form av tidigare forskning som publicerats i olika vetenskapliga tidskrifter.

Empiri: Empirin består av två stycken e-postintervjuer med representanter för Nordeas, samt Swedbanks Corporate Finance avdelningar. Vidare har intervjuer genomförts med representanter för fallföretaget. Vi har även tagit del av information från fallföretagets ekonomisystem i form av balans- och resultatrapporter.

Resultat: Denna studie visar att de lämpligaste värderingsmetoderna att använda vid värdering av ett företag i den specifika situation som vårt fallföretag befinner sig i, är kassaflödesmetoden samt residualvinstmetoden. Vidare visar studien att de vanligast använda värderingsmetoderna är multipelvärdering samt kassaflödesvärdering. Studien visar också att det är väldigt svårt att komma fram till ett exakt värde på ett företag då framtiden är oviss.

When valuing a company there exist various possible valuation methods to use. The reason behind this study is that the authors were contacted by the owners of a company, who where interested to know how much their company would be worth in the case of a possible sale. Specific with this company is that it only has one customer and almost no tangible assets.

Purpose: The purpose of this study is to make a survey of the different valuation methods that exist and to clarify which one is best suited in this particular case. This will result in a valuation of our case company.

Method: We have used a qualitative method in the shape of a thorough literary study and an exposition of earlier research in the area of company valuation. Furthermore we have made two interviews by email with representatives from the Corporate Finance departments of Swedbank and Nordea.

Theory:The theorethical framework of this study involves the different valuation methods that are described in the litterature that exists in the area. We have also shown some theory in the shape of earlier research that has been published in various scientific magazines.

Empirical foundation: The empirical foundation contains two interviews carried out by email with representatives from the Corporate Finance departments of Swedbank and Nordea. Interviews have also been made with representatives from our case company. The balance sheet and income statement from our case company’s economic system have also been studied.

Conclusion: This study shows that the most suitable valuation methods for our case company are the Discounted Cash Flow Model and the Residual Income Model. The study also shows that the most commonly used valuation methods are Multiple Valuation and Discounted Cash Flow Valuation. Finally the study shows that it is very difficult to reach one precise value when valuing a company with an uncertain future.

Salavová, Monika. "Stanovení hodnoty podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241544.

Väyrynen, Chytiris Ion, and Jonas Andersson. "Företagsvärdering och kriser : En eventstudie om coronapandemins effekt på de noterade företagens värdering på OMXS30." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85185.

Hjelström, Tomas. "The closed-end investment company premium puzzle : model development and empirical tests on Swedish and British data." Doctoral thesis, Handelshögskolan i Stockholm, Redovisning och Finansiering (B), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-480.

Chvostová, Ivana. "Ocenění podniku." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-15642.

Prachař, Pavel. "Ocenění podniku Toyota Peugeot Citroën Automobile Czech, s.r.o." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-163465.

Štěpánek, Michal. "Odhad hodnoty start-up projektu při využití metod strategického marketingu." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-110445.

Landström, Joachim. "The theory of Homo comperiens, the firm’s market price, and the implication for a firm’s profitability." Doctoral thesis, Uppsala universitet, Företagsekonomiska institutionen, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8268.

Blažková, Dana. "Určování hodnoty podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2021. http://www.nusl.cz/ntk/nusl-444234.

Svitaňová, Mária. "Určení hodnoty podniku Environchem." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222852.

Straňák, Peter. "Ocenění podniku společnosti Beznoska s.r.o." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75152.

Štěpánková, Jana. "Tržní oceňování podniku jako podklad pro strategická rozhodnutí." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2008. http://www.nusl.cz/ntk/nusl-376777.

Tsai, Ju-Fei, and 蔡如斐. "Business Valuation And Investment Strategy for 3M Company." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2wpx34.

Yang, Chih-Kuang, and 楊智光. "Case Study on Business Valuation – T Company as Example." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/22984775524721503634.

Chen, Cheng-I., and 陳正益. "CASE STUDY ON BUSINESS VALUATION - AN EXAMPLE ON IPC COMPANY." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8qx5ku.

Chung-HsienLiao and 廖忠賢. "Valuation of New Business Venture – A Case of P Company." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/s5z9sp.

Hsueh, Hsiaowen, and 薛筱玟. "An Analysis of Business Valuation-The Case of A Bike Company." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/87362363534780403132.

Morgado, Pedro Laranjeiro Amaro Serrão. "Equity valuation: The Navigator Company, S.A." Master's thesis, 2020. http://hdl.handle.net/10071/21566.

Lin, Yu-Lan, and 林玉蘭. "The Study on Business Valuation for M&A Hi-tech company." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/65829889801041918923.

Chang, Wei Fen, and 張維棻. "A Study on the Business Valuation–An Example on S Technology Company." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/63572249083605868024.

Ho, Chien-chih, and 何建志. "An Analysis of Business Valuation— The Case Study of An Airline Company." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/61090980332562128814.

Chen, Shou-Cheng, and 陳守正. "The Study on Business Valuation –The Case of Initial Public Offering LED Company." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/42555596883636276411.

Yu, Chia-Ling, and 余佳玲. "Design a Business Valuation Module by Excel VBA-The Case of S Company." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9ra6k4.

Lee, Mei-Li, and 李美麗. "The Research on the business Valuation and Value Creation Strategy of M Company." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/94301597754177081251.

Yao, Jiayu. "Valuation of a fintech company: Lending Club." Master's thesis, 2018. http://hdl.handle.net/10071/18806.

WEI, HUANG FEI, and 黃斐微. "A Study on the Business Valuation & Value Creation – An Example on C Company." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/13895331956866026428.

Bruinette, Albert J. M. "Relative importance of company financial statements in investment analysis." Thesis, 2014. http://hdl.handle.net/10210/9105.

CHANG, HUANG-YI, and 張凰誼. "A Study on the Business Valuation & Value Creation Strategies-An Example on M Company." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/k82pb2.

Peng, Kang-Yen, and 彭康晏. "A Study on the Business Valuation & Value Creation Strategies–An Example on H Motor Company." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/h9pec4.

CHEN, HUI-YEN, and 陳慧燕. "The Study on the Business Valuation & Value Creation Strategies-The Case Study of B Company." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/v5fu2t.

CHEN, HUNG-CHENG, and 陳鴻政. "The Study on the Business Valuation & Value Creation Strategies-The Case Study of A Company." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qxbmhv.

Maharaj, Chandradeep. "The valuation of the management buy-out of an unlisted company : (a case study)." Thesis, 2003. http://hdl.handle.net/10413/3871.

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• Josh Leidich
• Boston Logan
• Sabrina Young
• Princess Egbo (Priority)
• Sam Gaerke (CDME)
• Summer Geissman (Honda)
• Hayley Gregor (Huntington)
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The business value of design

We all know examples of bad product and service design. The USB plug (always lucky on the third try). The experience of rushing to make your connecting flight at many airports. The exhaust port on the Death Star in  Star Wars .

We also all know iconic designs, such as the Swiss Army Knife, the humble Google home page, or the Disneyland visitor experience.

Despite the obvious commercial benefits of designing great products and services, consistently realizing this goal is notoriously hard—and getting harder. Companies need stronger design capabilities than ever before.

So how do companies deliver exceptional designs, launch after launch? What is design worth? To answer these questions, we have conducted what we believe to be (at the time of writing) the most extensive and rigorous research undertaken anywhere to study the design actions that leaders can make to unlock business value.

We tracked the design practices of 300 publicly listed companies over a five-year period in multiple countries and industries. Their senior business and design leaders were interviewed or surveyed. Our team collected more than two million pieces of financial data and recorded more than 100,000 design actions. Advanced regression analysis uncovered the 12 actions showing the greatest correlation with improved financial performance and clustered these actions into four broad themes.

The four themes of good design described below form the basis of the McKinsey Design Index (MDI), which rates companies by how strong they are at design and—for the first time—how that links up with the financial performance of each company (Exhibit 1).

• We found a strong correlation between high MDI scores and superior business performance.
• The results held true in all three of the industries we looked at:  medical technology, consumer goods, and retail banking.
• TRS and revenue differences between the fourth, third, and second quartiles were marginal.  

In short, the potential for design-driven growth is enormous in both product- and service-based sectors. The good news is that there are more opportunities than ever to pursue user-centric, analytically informed design today.

Over 40 percent of the companies surveyed still aren’t talking to their end users during development. Just over 50 percent admitted that they have no objective way to assess or set targets for the output of their design teams.

Top-quartile companies in design—and leading financial performers—excelled in all four areas. What’s more, leaders appear to have an implicit understanding of the MDI themes. When senior executives were asked to name their organizations’ single greatest design weakness, 98 percent of the responses mapped to the four themes of the MDI.

We realize that many companies apply some of these design practices—a strong voice in the C-suite, for example, or shared design spaces. Our results, however, show that excellence across all four dimensions, which is required to reach the top quartile, is relatively rare. We believe this helps account for the dramatic range of design performance reflected in the observed companies’ MDI scores, which were as low as 43 and as high as 92.

The diversity among companies achieving top-quartile MDI performance shows that design excellence is within the grasp of every business, whether product, service, or digitally oriented.

The McKinsey Design Index highlights four key areas of action companies must take to join the top quartile of design performers. First, at the top of the organization, adopt an analytical approach to design by measuring and leading your company’s performance in this area with the same rigor the company devotes to revenues and costs. Second, put the user experience front and center in the company’s culture by softening internal boundaries (between physical products, services, and digital interactions, for example) that don’t exist for customers. Third, nurture your top design people and empower them in cross-functional teams that take collective accountability for improving the user experience while retaining the functional connections of their members. Finally, iterate, test, and learn rapidly, incorporating user insights from the first idea until long after the final launch.

Reflexive Analysis

The later half of the article goes into more detail of what these findings may mean, and I will echo them here.

The best performing companies were ones that allowed for designers to be in positions of leadership and decision-making. McKinsey noticed that stagnant companies like T-Mobile had a current CEO with the personal motto: “shut up and listen.” Contrastingly, more loved and successful companies such as IKEA and Pixar are integrating customer feedback sessions with higher-ups, the “dethroning” of the C-suite, and narrowing of the administrative gap.

McKinsey deduced that the user-experience is just as valuable as the product being sold, Walt Disney World being the quintessential example. What’s notable, however, is their emphasis on leaning into the future of smartphones and the digital/automated spaces. Companies must combine physical products, digital tools, and “pure” services to maximize customer retention.

Design departments aren’t treated with the same respect as the engineering and marketing departments, and this gap of haughty pretention isolates the designers as aloof creatives, dismissing their necessity in company-wide systems. Cooperation and collaboration was a sign of success, McKinsey found. We must overcome isolationist tendencies!

A baffling 50 percent of the companies interviewed claimed they had not conducted user research before generating their first design ideas or specifications. McKinsey points out the inherent learning, testing, and iteration phases of design that facilitate good end goals. Despite the value of iteration, almost 60 percent of companies they used prototypes only for internal-production testing, late in the development process (compared to better performing companies prototyping from the start, constantly). The best results come from constantly blending qualitative and quantitative research.

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Bridging private equity’s value creation gap

For the past 40 years or so, private equity (PE) buyout managers largely invested capital in an environment of declining interest rates and escalating asset prices. During that period, they were able to rely on financial leverage, enhanced tax and debt structures, and increasing valuations on high-quality assets to generate outsize returns for investors and create value.

Times have changed , however. Since 2020, the cost of debt has increased and liquidity in debt markets is harder to access given current interest rates, asset valuations, and typical bank borrowing standards. Fund performance has suffered as a result: PE buyout entry multiples declined from 11.9 to 11.0 times EBITDA through the first nine months of 2023. 1 2024 Global Private Markets Review , McKinsey, March 2024.

Even as debt markets begin to bounce back, a new macroeconomic reality is setting in—one that requires more than just financial acumen to drive returns. Buyout managers now need to focus on operational value creation strategies for revenue growth, as well as margin expansion to offset compression of multiples and to deliver desired returns to investors.

Based on our years of research and experience working with a range of private-capital firms across the globe, we have identified two key principles to maximize operational value creation.

First, buyout managers should invest with operational value creation at the forefront . This means that in addition to strategic diligence, they should conduct operational diligence for new assets. Their focus should be on developing a rigorous, bespoke, and integrated approach to assessing top-line and operational efficiency. During the underwriting process, managers can also identify actions that could expand and improve EBITDA margins and growth rates during the holding period, identify the costs involved in this transformation, and create rough timelines to track the assets’ performance. And if they acquire the asset, the manager should: 1) clearly establish the value creation objectives before deal signing, 2) emphasize operational and top-line improvements after closing, and 3) pursue continual improvements in ways of working with portfolio companies. Meanwhile, for existing assets, the manager should ensure that the level of oversight and monitoring is closely aligned with the health of each asset.

Second, everyone should understand and have a hand in improving operations . Within the PE firm, the operating group and deal teams should work together to enable and hold portfolio companies accountable for the execution of the value creation plan. This begins with an explicit focus on “linking talent to value”—ensuring leaders with the right combination of skills and experience are in place and empowered to deliver the plan, improve internal processes, and build organizational capabilities.

In our experience, getting these two principles right can significantly improve PE fund performance. Our initial analysis of more than 100 PE funds with vintages after 2020 indicates that general partners that focus on creating value through asset operations achieve a higher internal rate of return—up to two to three percentage points higher, on average—compared with peers.

The case for operational efficiency

The ongoing macroeconomic uncertainty has made it difficult for buyout managers to achieve historical levels of returns in the PE buyout industry using old ways of value creation. 2 Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later, and exited 2021 or before, can be attributed to market multiple expansion and leverage. See 2024 Global Private Markets Review .   And it’s not going to get any easier anytime soon, for two reasons.

Higher-for-longer rates will trigger financing issues

The US Federal Reserve projects that the federal funds rate will remain around 4.5 percent through 2024, then potentially drop to about 3.0 percent by the end of 2026. 3 “Summary of economic projections,” Federal Reserve Board, December 13, 2023.   Yet, even if rates decline by 200 basis points over the next two years, they will still be higher than they were over the past four years when PE buyout deals were underwritten.

This could create issues with recapitalization or floating interest rate resets for a portfolio company’s standing debt. Consider that the average borrower takes a leveraged loan at an interest coverage ratio of about three times EBIDTA (or 3x). 4 The interest coverage ratio is an indicator of a borrower’s ability to service debt, or potential default risk.   With rising interest expenses and additional profitability headwinds, these coverage ratios could quickly fall below 2x and get close to or trip covenant triggers around 1x. In 2023, for example, the average leveraged loan in the healthcare and software industries was already at less than a 2x interest coverage ratio. 5 James Gelfer and Stephanie Rader, “What’s the worst that could happen? Default and recovery rates in private credit,” Goldman Sachs, April 20, 2023.   To avoid a covenant breach, or (if needed) increasing recapitalization capital available without equity paydown, managers will need to rely on operational efficiency to increase EBITDA.

Valuations are mismatched

If interest rates remain high, the most recent vintage of PE assets is likely to face valuation mismatches at exit, or extended hold periods until value can be realized. Moreover, valuation of PE assets has remained high relative to their public-market equivalents, partly a result of the natural lag in how these assets are marked to market. As the CEO of Harvard University’s endowment explained in Harvard’s 2023 annual report, it will likely take more time for private valuations to fully reflect market conditions due to the continued slowdown in exits and financing rounds. 6 Message from the CEO of Harvard Management Company, September 2023.

Adapting PE’s value creation approach

Operational efficiency isn’t a new concept in the PE world. We’ve previously written  about the strategic shift among firms, increasingly notable since 2018, moving from the historical “buy smart and hold” approach to one of “acquire, align on strategy, and improve operating performance.”

However, the role of operations in creating more value is no longer just a source of competitive advantage but a competitive necessity for managers. Let’s take a closer look at the two principles that can create operational efficiency.

Invest with operational value creation at the forefront

PE fund managers can improve the profitability and exit valuations of assets by having operations-related conversations up front.

Assessing new assets. Prior to acquiring an asset, PE managers typically conduct financial and strategic diligence to refine their understanding of a given market and the asset’s position in that market. They should also undertake operational diligence—if they are not already doing so—to develop a holistic view of the asset to inform their value creation agenda.

Operational diligence involves the detailed assessment of an asset’s operations, including identification of opportunities to improve margins or accelerate organic growth. A well-executed operational-diligence process can reveal or confirm which types of initiatives could generate top-line and efficiency-driven value, the estimated cash flow improvements these initiatives could generate, the approximate timing of any cash flow improvements, and the potential costs of such initiatives.

The results of an operational-diligence process can be advantageous in other ways, too. Managers can use the findings to create a compelling value creation plan, or a detailed memo summarizing the near-term improvement opportunities available in the current profit-and-loss statement, as well as potential opportunities for expansion into adjacencies or new markets. After this step is done, they should determine, in collaboration with their operating-group colleagues, whether they have the appropriate leaders in place to successfully implement the value creation plan.

These results can also help managers resolve any potential issues up front, prior to deal signing, which in turn could increase the likelihood of receiving investment committee approval for the acquisition. Managers also can share the diligence findings with co-investors and financiers to help boost their confidence in the investment and the associated value creation thesis.

It is crucial that managers have in-depth familiarity with company operations, since operational diligence is not just an analytical-sizing exercise. If they perform operational diligence well, they can ensure that the full value creation strategy and performance improvement opportunities are embedded in the annual operating plan and the longer-term three- to five-year plan of the portfolio company’s management team.

Assessing existing assets. When it comes to existing assets, a fundamental question for PE managers is how to continue to improve performance throughout the deal life cycle. Particularly in the current macroeconomic and geopolitical environment, where uncertainty reigns, managers should focus more—and more often—on directly monitoring assets and intervening when required. They can complement this monitoring with routine touchpoints with the CEO, CFO, and chief transformation officer (CTO) of individual assets to get updates on critical initiatives driving the value creation plan, along with ensuring their operating group has full access to each portfolio company’s financials. Few PE managers currently provide this level of transparency into their assets’ performance.

To effectively monitor existing assets, managers can use key performance indicators (KPIs) directly linked to the fund’s investment thesis. For instance, if the fund’s investment thesis is centered on the availability of inventory, they may rigorously track forecasts of supply and demand and order volumes. This way, they can identify and address issues with inventory early on. Some managers pull information directly from the enterprise resource planning systems in their portfolio companies to get full visibility into operations. Others have set up specific “transformation management offices” to support performance improvements in key assets and improve transparency on key initiatives.

We’ve seen managers adopt various approaches with assets that are on track to meet return hurdles. They have frequent discussions with the portfolio company’s management team, perform quarterly credit checks on key suppliers and customers to ensure stability of their extended operations, and do a detailed review of the portfolio company’s operations and financial performance two to three years into the hold period. Managers can therefore confirm whether the management team is delivering on their value creation plans and also identify any new opportunities associated with the well-performing assets.

If existing assets are underperforming or distressed, managers’ prompt interventions to improve operations in the near term, and improve revenue over the medium term, can determine whether they should continue to own the asset or reduce their equity position through a bankruptcy proceeding. One manager implemented a cash management program to monitor and improve the cash flow for an underperforming retail asset of a portfolio company. The approach helped the portfolio company overcome a peak cash flow crisis period, avoid tripping liquidity covenants in an asset-backed loan, and get the time needed for the asset’s long-term performance to improve.

Reassess internal operations and governance

In addition to operational improvements, managers should also assess their own operations and consider shifting to an operating model that encourages increased engagement between their team and the portfolio companies. They should cultivate a stable of trusted, experienced executives within the operating group. They should empower these executives to be equal collaborators with the deal team in determining the value available in the asset to be underwritten, developing an appropriate value creation strategy, and overseeing performance of the portfolio company’s management.

Shift to a ‘just right’ operating model for operating partners. The operating model through which buyout managers engage with portfolio companies should be “just right”—that is, aligned with the fund’s overall strategy, how the fund is structured, and who sets the strategic vision for each individual portfolio company.

There are two types of engagement operating models—consultative and directive. When choosing an operating model, firms should align their hiring and internal capabilities to support their operating norms, how they add value to their portfolio companies, and the desired relationship with the management team (exhibit).

Take the example of a traditional buyout manager that acquires good companies with good management teams. In such a case, the portfolio company’s management team is likely to already have a strategic vision for the asset. These managers may therefore choose a more consultative engagement approach (for instance, providing advice and support to the portfolio company for any board-related issues or other challenges).

For value- or operations-focused funds, the manager may have higher ownership in the strategic vision for the asset, so their initial goal should be to develop a management team that can deliver on a specific investment thesis. In this case, the support required by the portfolio company could be less specialized (for example, the manager helps in hiring the right talent for key functional areas), and more integrative, to ensure a successful end-to-end transformation for the asset. As such, a more directive or oversight-focused engagement operating model may be preferred.

Successful execution of these engagement models requires the operating group to have the right talent mix and experience levels. If the manager implements a “generalist” coverage model, for example, where the focus is on monitoring and overseeing portfolio companies, the operating group will need people with the ability (and experience) to support the management in end-to-end transformations. However, a different type of skill set is required if the manager chooses a “specialist” coverage model, where the focus is on providing functional guidance and expertise (leaving transformations to the portfolio company’s management teams). Larger and more mature operating groups frequently use a mix of both talent pools.

Empower the operating group. In the past, many buyout managers did not have operating teams, so they relied on the management teams in the portfolio companies to fully identify and implement the value creation plan while running the asset’s day-to-day operations. Over time, many top PE funds began to establish internal operating groups  to provide strategic direction, coaching, and support to their portfolio companies. The operating groups, however, tended to take a back seat to deal teams, largely because legacy mindsets and governance structures placed responsibility for the performance of an asset on the deal team. In our view, while the deal team needs to remain responsible and accountable for the deal, certain tasks can be delegated to the operating group.

Some managers give their operating group members seats on portfolio company boards, hiring authority for key executives, and even decision-making rights on certain value creation strategies within the portfolio. For optimal performance, these operating groups should have leaders with prior C-suite responsibility or commensurate accountability within the PE fund and experience executing cross-functional mandates and company transformations. Certain funds with a core commitment to portfolio value creation include the leader of the operating group on the investment committee. Less-experienced members of the operating group can have consultative arrangements or peer-to-peer relationships with key portfolio company leaders.

Since the main KPIs for operating teams are financial, it is critical that their leaders understand a buyout asset’s business model, financing, and general market dynamics. The operating group should also be involved in the deal during the diligence phase, and participate in the development of the value creation thesis as well as the underwriting process. Upon deal close, the operating team should be as empowered as the deal team to serve as stewards of the asset and resolve issues concerning company operations.

Some funds also are hiring CTOs  for their portfolio companies to steer them through large transformations. Similar to the CTO in any organization , they help the organization align on a common vision, translate strategy into concrete initiatives for better performance, and create a system of continuous improvement and growth for the employees. However, when deployed by the PE fund, the CTO also often serves as a bridge between the PE fund and the portfolio company and can serve as a plug-and-play executive to fill short-term gaps in the portfolio company management team. In many instances, the CTO is given signatory, and occasionally broader, functional responsibilities. In addition, their personal incentives can be aligned with the fund’s desired outcomes. For example, funds may tie an element of the CTO’s overall compensation to EBITDA improvement or the success of the transformation.

Bring best-of-breed capabilities to portfolio companies. Buyout managers can bring a range of compelling capabilities to their portfolio companies, especially to smaller and midmarket companies and their internal operating teams. Our conversations with industry stakeholders revealed that buyout managers’ skills can be particularly useful in the following three areas:

• Procurement. Portfolio companies can draw on a buyout manager’s long-established procurement processes, team, and negotiating support. For instance, managers often have prenegotiated rates with suppliers or group purchasing arrangements that portfolio companies can leverage to minimize their own procurement costs and reduce third-party spending.
• Executive talent. They can also capitalize on the diverse and robust network of top talent that buyout managers have likely cultivated over time, including homegrown leaders and ones found through executive search firms (both within and outside the PE industry).
• Partners. Similarly, they can work with the buyout manager’s roster of external experts, business partners, suppliers, and advisers to find the best solutions to their emerging business challenges (for instance, gaining access to offshore resources during a carve-out transaction).

Ongoing macroeconomic uncertainty is creating unprecedented times in the PE buyout industry. Managers should use this as an opportunity to redouble their efforts on creating operational improvements in their existing portfolio, as well as new assets. It won’t be easy to adapt and evolve value creation processes and practices, but managers that succeed have an opportunity to close the gap between the current state of value creation and historical returns and outperform their peers.

Jose Luis Blanco is a senior partner in McKinsey’s New York office, where Matthew Maloney is a partner; William Bundy is a partner in the Washington, DC, office; and Jason Phillips is a senior partner in the London office.

The authors wish to thank Louis Dufau and Bill Leigh for their contributions to this article.

This article was edited by Arshiya Khullar, an editor in McKinsey’s Gurugram office.

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

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Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

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MIT CISR wishes to thank all of our associate members for their support and contributions.

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MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

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McDonald's $5 value meal is coming in June — and staying for just a month

McDonald’s  is set to offer a $5 value meal in the U.S., but only for a limited time.

The promotion will include four items for $5 — a McChicken or McDouble, four-piece chicken nuggets, fries and a drink — and will run for roughly a month, beginning on June 25, according to a person familiar with the offering who was not authorized to speak about it publicly.

“We know how much it means to our customers when McDonald’s offers meaningful value and communicates it through national advertising. That’s been true since our very beginning and never more important than it is today,” McDonald’s said in a statement to CNBC.

CNBC last week reported the fast-food giant was  working to bring a value offering to menus , with details being discussed and voted on by franchisees. An initial proposal for the meal did not clear necessary hurdles.

Coca-Cola  added marketing funds to the equation to make the deal more appealing, CNBC reported Friday. In a statement on Wednesday, Coca-Cola said: “We routinely partner with our customers on marketing programs to meet consumer needs. This helps us grow our businesses together.”

Financial terms of that partnership were not disclosed.

The monthlong promotion comes at a time when restaurants are finally beginning to  feel a long-anticipated consumer pullback.

McDonald’s  recently reported a mixed first quarter , with U.S. same-store sales slightly missing expectations. Higher prices helped grow average checks, but some consumers pulled back as a result of the steeper costs.

“Consumers continue to be even more discriminating with every dollar that they spend as they faced elevated prices in their day-to-day spending, which is putting pressure on the [quick-service restaurant] industry,” CEO Chris Kempczinski said on the company’s earnings call on April 30.

He added McDonald’s has to be “laser-focused” on affordability to attract diners.

“Great value and affordability have always been a hallmark of McDonald’s brand, and all three legs of the stool are coming together to deliver that at a time when our customers really need it. This is the power and promise of the Golden Arches,” John Palmaccio, McDonald’s owner and operator and chair of the Operators National Advertising Fund, said in a statement to CNBC on the $5 promotion.

— CNBC’s Amelia Lucas contributed to this report.

More from CNBC:

• Here’s the inflation breakdown for April 2024 — in one chart
• S&P 500, Nasdaq rise to all-time highs after light consumer inflation report
• Boeing breached '21 deal that shielded it from criminal charges over 737 Max crashes, DOJ says

Kate Rogers covers restaurants and small business for CNBC.