empirical research business model

Business model innovation: a review and research agenda

New England Journal of Entrepreneurship

ISSN : 2574-8904

Article publication date: 16 October 2019

Issue publication date: 13 November 2019

The aim of this paper is to review and synthesise the recent advancements in the business model literature and explore how firms approach business model innovation.

Design/methodology/approach

A systematic review of business model innovation literature was carried out by analysing 219 papers published between 2010 and 2016.

Evidence reviewed suggests that rather than taking either an evolutionary process of continuous revision, adaptation and fine-tuning of the existing business model or a revolutionary process of replacing the existing business model, firms can explore alternative business models through experimentation, open and disruptive innovations. It was also found that changing business models encompasses modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements in four areas of innovation: value proposition, operational value, human capital and financial value.

Research limitations/implications

Although this review highlights the different avenues to business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain unexplored.

Practical implications

The business model innovation framework can be used by practitioners as a “navigation map” to determine where and how to change their existing business models.

Originality/value

Because conflicting approaches exist in the literature on how firms change their business models, the review synthesises these approaches and provides a clear guidance as to the ways through which business model innovation can be undertaken.

• Business model
• Value proposition
• Value creation
• Value capture

Ramdani, B. , Binsaif, A. and Boukrami, E. (2019), "Business model innovation: a review and research agenda", New England Journal of Entrepreneurship , Vol. 22 No. 2, pp. 89-108. https://doi.org/10.1108/NEJE-06-2019-0030

Emerald Publishing Limited

Copyright © 2019, Boumediene Ramdani, Ahmed Binsaif and Elias Boukrami

Published in New England Journal of Entrepreneurship . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Firms pursue business model innovation by exploring new ways to define value proposition, create and capture value for customers, suppliers and partners ( Gambardella and McGahan, 2010 ; Teece, 2010 ; Bock et al. , 2012 ; Casadesus-Masanell and Zhu, 2013 ). An extensive body of the literature asserts that innovation in business models is of vital importance to firm survival, business performance and as a source of competitive advantage ( Demil and Lecocq, 2010 ; Chesbrough, 2010 ; Amit and Zott, 2012 ; Baden-Fuller and Haefliger, 2013 ; Casadesus-Masanell and Zhu, 2013 ). It is starting to attract a growing attention, given the increasing opportunities for new business models enabled by changing customer expectations, technological advances and deregulation ( Casadesus-Masanell and Llanes, 2011 ; Casadesus-Masanell and Zhu, 2013 ). This is evident from the recent scholarly outputs ( Figure 1 ). Thus, it is essential to comprehend this literature and uncover where alternative business models can be explored.

Conflicting approaches exist in the literature on how firms change their business models. One approach suggests that alternative business models can be explored through an evolutionary process of incremental changes to business model elements (e.g. Demil and Lecocq, 2010 ; Dunford et al. , 2010 ; Amit and Zott, 2012 ; Landau et al. , 2016 ; Velu, 2016 ). The other approach, mainly practice-oriented, advocates that innovative business models can be developed through a revolutionary process by replacing existing business models (e.g. Bock et al. , 2012 ; Iansiti and Lakhani, 2014 ). The fragmentation of prior research is due to the variety of disciplinary and theoretical foundations through which business model innovation is examined. Scholars have drawn on perspectives from entrepreneurship (e.g. George and Bock, 2011 ), information systems (e.g. Al-debei and Avison, 2010 ), innovation management (e.g. Dmitriev et al. , 2014 ), marketing (e.g. Sorescu et al. , 2011 ) and strategy (e.g. Demil and Lecocq, 2010 ). Also, this fragmentation is deepened by focusing on different types of business models in different industries. Studies have explored different types of business models such as digital business models (e.g. Weill and Woerner, 2013 ), service business models (e.g. Kastalli et al. , 2013 ), social business models (e.g. Hlady-Rispal and Servantie, 2016 ) and sustainability-driven business models ( Esslinger, 2011 ). Besides, studies have examined different industries such as airline ( Lange et al. , 2015 ), manufacturing ( Landau et al. , 2016 ), newspaper ( Karimi and Walter, 2016 ), retail ( Brea-Solís et al. , 2015 ) and telemedicine ( Peters et al. , 2015 ).

Since the first comprehensive review of business model literature was carried out by Zott et al. (2011) , several reviews were published recently (as highlighted in Table I ). Our review builds on and extends the extant literature in at least three ways. First, unlike previous reviews that mainly focused on the general construct of “Business Model” ( George and Bock, 2011 ; Zott et al. , 2011 ; Wirtz et al. , 2016 ), our review focuses on uncovering how firms change their existing business model(s) by including terms that reflect business model innovation, namely, value proposition, value creation and value capture. Second, previous reviews do not provide a clear answer as to how firms change their business models. Our review aims to provide a clear guidance on how firms carry out business model innovation by synthesising the different perspectives existing in the literature. Third, compared to recent reviews on business model innovation ( Schneider and Spieth, 2013 ; Spieth et al. , 2014 ), which have touched lightly on some innovation aspects such as streams and motivations of business model innovation research, our review will uncover the innovation areas where alternative business models can be explored. Taking Teece’s (2010) suggestion, “A helpful analytic approach for management is likely to involve systematic deconstruction/unpacking of existing business models, and an evaluation of each element with an idea toward refinement or replacement” (p. 188), this paper aims to develop a theoretical framework of business model innovation.

Our review first explains the scope and the process of the literature review. This is followed by a synthesis of the findings of the review into a theoretical framework of business model innovation. Finally, avenues for future research will be discussed in relation to the approaches, degree and mechanisms of business model innovation.

2. Scope and method of the literature review

Given the diverse body of business models literature, a systematic literature review was carried out to minimise research bias ( Transfield et al. , 2003 ). Compared to the previous business model literature, our review criteria are summarised in Table I . The journal papers considered were published between January 2010 and December 2016. As highlighted in Figure 1 , most contributions in this field have been issued within this period since previous developments in the literature were comprehensively reviewed up to the end of 2009 ( Zott et al. , 2011 ). Using four databases (EBSCO Business Complete, ABI/INFORM, JSTOR and ScienceDirect), we searched peer-reviewed papers with terms such as business model(s), innovation value proposition, value creation and value capture appearing in the title, abstract or subject terms. As a result, 8,642 peer-reviewed papers were obtained.

Studies were included in our review if they specifically address business models and were top-rated according to The UK Association of Business Schools list ( ABS, 2010 ). This rating has been used not only because it takes into account the journal “Impact Factor” as a measure for journal quality, but also uses in conjunction other measures making it one of the most comprehensive journal ratings. By applying these criteria, 1,682 entries were retrieved from 122 journals. By excluding duplications, 831 papers were identified. As Harvard Business Review is not listed among the peer-reviewed journals in any of the chosen databases and was included in the ABS list, we used the earlier criteria and found 112 additional entries. The reviewed papers and their subject fields are highlighted in Table II . Since the focus of this paper is on business model innovation, we selected studies that discuss value proposition, value creation and value capture as sub-themes. This is not only because the definition of business model innovation mentioned earlier spans all three sub-themes, but also because all three sub-themes have been included in recent studies (e.g. Landau et al. , 2016 ; Velu and Jacob, 2014 ). To confirm whether the papers addressed business model innovation, we examined the main body of the papers to ensure they were properly coded and classified. At the end of the process, 219 papers were included in this review. Table III lists the source of our sample.

The authors reviewed the 219 papers using a protocol that included areas of innovation (i.e. components, elements, and activities), theoretical perspectives and key findings. In order to identify the main themes of business model innovation research, all papers were coded in relation to our research focus as to where alternative business models can be explored (i.e. value proposition, value creation and value capture). Coding was cross checked among the authors on a random sample suggesting high accuracy between them. Having compared and discussed the results, the authors were able to identify the main themes.

3. Prior conceptualisations of business model innovation

Some scholars have articulated the need to build the business model innovation on a more solid theoretical ground ( Sosna et al. , 2010 ; George and Bock, 2011 ). Although many studies are not explicitly theory-based, some studies partially used well-established theories such as the resource-based view (e.g. Al-Debei and Avison, 2010 ) and transaction cost economics (e.g. DaSilva and Trkman, 2014 ) to conceptualise business model innovation. Other theories such as activity systems perspective, dynamic capabilities and practice theory have been used to help answer the question of how firms change their existing business models.

Using the activity systems perspective, Zott and Amit (2010) demonstrated how innovative business models can be developed through the design themes that describe the source of value creation (novelty, lock-in, complementarities and efficiency) and design elements that describe the architecture (content, structure and governance). This work, however, overlooks value capture which limits the explanation of the advocated system’s view (holistic). Moreover, Chatterjee (2013) used this perspective to reveal that firms can design innovative business models that translate value capture logic to core objectives, which can be delivered through the activity system.

Dynamic capability perspective frames business model innovation as an initial experiment followed by continuous revision, adaptation and fine-tuning based on trial-and-error learning ( Sosna et al. , 2010 ). Using this perspective, Demil and Lecocq (2010) showed that “dynamic consistency” is a capability that allows firms to sustain their performance while innovating their business models through voluntary and emergent changes. Also, Mezger (2014) conceptualised business model innovation as a distinct dynamic capability. He argued that this capability is the firm’s capacity to sense opportunities, seize them through the development of valuable and unique business models, and accordingly reconfigure the firms’ competences and resources. Using aspects of practice theory, Mason and Spring (2011) looked at business model innovation in the recorded sound industry and found that it can be achieved through various combinations of managerial practices.

Static and transformational approaches have been used to depict business models ( Demil and Lecocq, 2010 ). The former refers to viewing business models as constituting core elements that influence business performance at a particular point in time. This approach offers a snapshot of the business model elements and how they are assembled, which can help in understanding and communicating a business model (e.g. Eyring et al. , 2011 ; Mason and Spring, 2011 ; Yunus et al. , 2010). The latter, however, focuses on innovation and how to address the changes in business models over time (e.g. Sinfield et al. , 2012 ; Girotra and Netessine, 2014 ; Landau et al. , 2016 ). Some researchers have identified the core elements of business models ex ante (e.g. Demil and Lecocq, 2010 ; Wu et al. , 2010 ; Huarng, 2013 ; Dmitriev et al. , 2014 ), while others argued that considering a priori elements can be restrictive (e.g. Casadesus-Masanell and Ricart, 2010 ). Unsurprisingly, some researchers found a middle ground where elements are loosely defined allowing flexibility in depicting business models (e.g. Zott and Amit, 2010 ; Sinfield et al. , 2012 ; Kiron et al. , 2013 ).

Prior to 2010, conceptual frameworks focused on the business model concept in general (e.g. Chesbrough and Rosenbloom, 2002 ; Osterwalder et al. , 2005 ; Shafer et al. , 2005 ) apart from Johnson et al. ’s (2008 ), which is one of the early contributions to business model innovation. To determine whether a change in existing business model is necessary, Johnson et al. (2008) suggested three steps: “Identify an important unmet job a target customer needs done; blueprint a model that can accomplish that job profitably for a price the customer is willing to pay; and carefully implement and evolve the model by testing essential assumptions and adjusting as you learn” ( Eyring et al. , 2011 , p. 90). Although several frameworks have been developed since then, our understanding of business model innovation is still limited due to the static nature of the majority of these frameworks. Some representations ignore the elements and/or activities where alternative business models can be explored (e.g. Sinfield et al. , 2012 ; Chatterjee, 2013 ; Huarng, 2013 ; Morris et al. , 2013 ; Dmitriev et al. , 2014 ; Girotra and Netessine, 2014 ). Other frameworks ignore value proposition (e.g. Zott and Amit, 2010 ), ignore value creation (e.g. Dmitriev et al. , 2014 ; Michel, 2014 ) and/or ignore value capture (e.g. Mason and Spring, 2011 ; Sorescu et al. , 2011 ; Storbacka, 2011 ). Some conceptualisations do not identify who is responsible for the innovation (e.g. Casadesus-Masanell and Ricart, 2010 ; Sinfield et al. , 2012 ; Chatterjee, 2013 ; Kiron et al. , 2013 ). Synthesising the different contributions into a theoretical framework of business model innovation will enable a better understanding of how firms undertake business model innovation.

4. Business model innovation framework

Our framework ( Figure 2 ) integrates all the elements where alternative business models can be explored. This framework does not claim that the listed elements are definitive for high-performing business models, but is an attempt to outline the elements associated with business model innovation. This framework builds on the previous work of Johnson et al. (2008) and Zott and Amit (2010) by signifying the elements associated with business model innovation. Unlike previous frameworks that mainly consider the constituting elements of business models, this framework focuses on areas of innovation where alternative business models can be explored. Moreover, this is not a static view of the constituting elements of a business model, but rather a view enabling firms to explore alternative business models by continually refining these elements. Arrows in the framework indicate the continuous interaction of business model elements. This framework consists of 4 areas of innovation and 16 elements (more details are shown in Table IV ). Each will be discussed below.

4.1 Value proposition

The first area of innovation refers to elements associated with answering the “Why” questions. While most of the previously established models in the literature include at least one of the value proposition elements (e.g. Brea-Solís et al. , 2015 ; Christensen et al. , 2016 ), other frameworks included two elements (e.g. Dahan et al. , 2010 ; Cortimiglia et al. , 2016 ) and three elements (e.g. Eyring et al. , 2011 ; Sinfield et al. , 2012 ). These elements include rethinking what a company sells, exploring new customer needs, acquiring target customers and determining whether the benefits offered are perceived by customers. Modern organisations are highly concerned with innovation relating to value proposition in order to attract and retain a large portion of their customer base ( Al-Debei and Avison, 2010 ). Developing new business models usually starts with articulating a new customer value proposition ( Eyring et al. , 2011 ). According to Sinfield et al. (2012) , firms are encouraged to explore various alternatives of core offering in more depth by examining type of offering (product or service), its features (custom or off-the-shelf), offered benefits (tangible or intangible), brand (generic or branded) and lifetime of the offering (consumable or durable).

In order to exploit the “middle market” in emerging economies, Eyring et al. (2011) suggested that companies need to design new business models that aim to meet unsatisfied needs and evolve these models by continually testing assumptions and making adjustments. To uncover unmet needs, Eyring et al. (2011) suggested answering four questions: what are customers doing with the offering? What alternative offerings consumers buy? What jobs consumers are satisfying poorly? and what consumers are trying to accomplish with existing offerings? Furthermore, Baden-Fuller and Haefliger (2013) made a distinction between customers and users in two-sided platforms, where users search for products online, and customers (firms) place ads to attract users. They also made a distinction between “pre-designed (scale) based offerings” and “project based offerings”. While the former focuses on “one-size-fits-all”, the latter focuses on specific client solving specific problem.

Established firms entering emerging markets should identify unmet needs “the job to be done” rather than extending their geographical base for existing offerings ( Eyring et al. , 2011 ). Because customers in these markets cannot afford the cheapest of the high-end offerings, firms with innovative business models that meet these customers’ needs affordably will have opportunities for growth ( Eyring et al. , 2011 ). Moreover, secondary business model innovation has been advocated by Wu et al. (2010) as a way for latecomer firms to create and capture value from disruptive technologies in emerging markets. This can be achieved through tailoring the original business model to fit price-sensitive mass customers by articulating a value proposition that is attractive for local customers.

4.2 Operational value

The second area of innovation focuses on elements associated with answering the “What” questions. Many of the established frameworks included either one element (e.g. Sinfield et al. , 2012 ; Taran et al. , 2015 ), two elements (e.g. Mason and Spring, 2011 ; Dmitriev et al. , 2014 ). However, very few included three or more elements (e.g. Mehrizi and Lashkarbolouki, 2016 ; Cortimiglia et al. , 2016 ). These elements include configuring key assets and sequencing activities to deliver the value proposition, exposing the various means by which a company reaches out to customers, and establishing links with key partners and suppliers. Focusing on value creation, Zott and Amit (2010) argued that business model innovation can be achieved through reorganising activities to reduce transaction costs. However, Al-Debei and Avison (2010) argued that innovation relating to this dimension can be achieved through resource configuration, which demonstrates a firm’s ability to integrate various assets in a way that delivers its value proposition. Cavalcante et al. (2011) proposed four ways to change business models: business model creation, extension, revision and termination by creating or adding new processes, and changing or terminating existing processes.

Western firms have had difficulty competing in emerging markets due to importing their existing business models with unchanged operating model ( Eyring et al. , 2011 ). Alternative business models can be uncovered when firms explore the different roles they might play in the industry value chain ( Sinfield et al. , 2012 ). Al-Debei and Avison (2010) suggested achieving this through answering questions such as: what is the position of our firm in the value system? and what mode of collaboration (open or close) would we choose to reach out in a business network? Dahan et al. (2010) found cross-sector partnerships as a way to co-create new multi-organisational business models. They argued that multinational enterprises (MNEs) can collaborate with nongovernmental organisations (NGOs) to create products/or services that neither can create on their own. Collaboration allows access to resources that firms would otherwise need to solely develop or purchase ( Yunus et al. , 2010 ). According to Wu et al. (2010) , secondary business model innovation can be achieved when latecomer firms fully utilise strategic partners’ complementary assets to overcome their latecomer disadvantages and build a unique value network specific to emerging economies context.

4.3 Human capital

The third area of innovation refers to elements associated with answering the “Who” questions. Most of the established frameworks in this field tend to focus less on human capital and include one element at most (e.g. Wu et al. , 2010 ; Kohler, 2015 ). However, our framework highlights four elements, which include experimenting with new ways of doing business, tapping into the skills and competencies needed for the new business model through motivating and involving individuals in the innovation process. According to Belenzon and Schankerman (2015) , “the ability to tap into a pool of talent is strongly related to the specific business model chosen by managers” (p. 795). They claimed that managers can strategically influence individuals’ contributions and their impact on project performance.

Organisational learning can be maximised though continuous experimentation and making changes when actions result in failure ( Yunus et al. , 2010 ). Challenging and questioning the existing rules and assumptions and imagining new ways of doing business will help develop new business models. Another essential element of business model design is governance, which refers to who performs the activities ( Zott and Amit, 2010 ). According to Sorescu et al. (2011) , innovation in retail business models can occur as a result of changes in the level of participation by actors engaged in performing the activities. An essential element of retailing governance is the incentive structure or the mechanisms that motivate those involved in carrying out their roles to meet customer demands ( Sorescu et al. , 2011 ). For example, discount retailers tend to establish different compensation and incentive policies ( Brea-Solís et al. , 2015 ). Revising the incentive system can have a major impact on new ventures’ performance by aligning organisational goals at each stage of growth ( Roberge, 2015 ). Zott and Amit (2010) argued that alternative business models can be explored through adopting innovative governance or changing one or more parties that perform any activities. Sinfield et al. (2012) suggested that business model innovation only requires time from a small team over a short period of time to move a company beyond incremental improvements and generate new opportunities for growth. This is supported by Michel’s (2014) finding that cross-functional teams were able to quickly achieve business model innovation in workshops through deriving new ways to capture value.

4.4 Financial value

The final area of innovation focuses on elements associated with answering the “How” questions. Previously developed frameworks tend to prioritise this area of innovation by three elements (e.g. Eyring et al. , 2011 ; Huang et al. , 2013 ), and in one instance four elements (e.g. Yunus et al. , 2010 ). These elements include activities linked with how to capture value through revenue streams, changing the price-setting mechanisms, and assessing the financial viability and profitability of a business. According to Demil and Lecocq (2010) , changes in cost and/or revenue structures are the consequences of both continuous and radical changes. They also argued that costs relate to different activities run by organisations to acquire, integrate, combine or develop resources. Michel (2014) suggested that alternative business models can be explored through: changing the price-setting mechanism, changing the payer, and changing the price carrier. Different innovation forms are associated with each of these categories.

Business model innovation can be achieved through exploring new ways to generate cash flows ( Sorescu et al. , 2011 ), where the organisation has to consider (and potentially change) when the money is collected: prior to the sale, at the point of sale, or after the sale ( Baden-Fuller and Haefliger, 2013 ). Furthermore, Demil and Lecocq (2010) suggested that changes in business models affect margins. This is apparent in the retail business models, which generate more profit through business model innovation compared to other types of innovation ( Sorescu et al. , 2011 ).

5. Ways to change business models

From reviewing the recent developments in the business model literature, alternative business models can be explored through modifying a single business model element, altering multiple elements simultaneously and/or changing the interactions between elements of a business model.

Changing one of the business model elements (i.e. content, structure or governance) is enough to achieve business model innovation ( Amit and Zott, 2012 ). This means that firms can have a new activity system by performing only one new activity. However, Amit and Zott (2012) clearly outlined a systemic view of business models which entails a holistic change. This is evident from Demil and Lecocq’s (2010) work suggesting that the study of business model innovation should not focus on isolated activities since changing a core element will not only impact other elements but also the interactions between these elements.

Another way to change business models is through altering multiple business model elements simultaneously. Kiron et al. (2013) found that companies combining target customers with value chain innovations and changing one or two other elements of their business models tend to profit from their sustainability activities. They also found that firms changing three to four elements of their business models tend to profit more from their sustainability activities compared to those changing only one element. Moreover, Dahan et al. (2010) found that a new business model was developed as a result of MNEs and NGOs collaboration by redefining value proposition, target customers, governance of activities and distribution channels. Companies can explore multiple combinations by listing different business model options they could undertake (desirable, discussable and unthinkable) and evaluate new combinations that would not have been considered otherwise ( Sinfield et al. , 2012 ).

Changing business models is argued to be demanding as it requires a systemic and holistic view ( Amit and Zott, 2012 ) by considering the relationships between core business model elements ( Demil and Lecocq, 2010 ). As mentioned earlier, changing one element will not only impact other elements but also the interactions between these elements. A firm’s resources and competencies, value proposition and organisational system are continuously interacting and this will in turn impact business performance either positively or negatively ( Demil and Lecocq, 2010 ). According to Zott and Amit (2010) , innovative business models can be developed through linking activities in a novel way that generates more value. They argued that alternative business models can be explored by configuring business model design elements (e.g. governance) and connecting them to distinct themes (e.g. novelty). Supporting this, Eyring et al. (2011) suggested that core business model elements need to be integrated in order to create and capture value ( Eyring et al. , 2011 ).

6. Discussion and future research directions

From the above synthesis of the recent development in the literature, several gaps remain unfilled. To advance the literature, possible future research directions will be discussed in relation to approaches, degrees and mechanisms of business model innovation.

6.1 Approaches of business model innovation

Experimentation, open innovation and disruption have been advocated as approaches to business model innovation. Experimentation has been emphasised as a way to exploit opportunities and develop alternative business models before committing additional investments ( McGrath, 2010 ). Several approaches have been developed to assist in business model experimentation including mapping approach, discovery-driven planning and trail-and-error learning ( Chesbrough, 2010 ; McGrath, 2010 ; Sosna et al. , 2010 ; Andries and Debackere, 2013 ). Little is known about the effectiveness of these approaches. It will be worth investigating which elements of the business model innovation framework are more susceptible to experimentation and which elements should be held unchanged. Although business model innovation tends to be characterised with failure ( Christensen et al. , 2016 ), not much has been established on failing business models. It is interesting to explore how firms determine a failing business model and what organisational processes exist (if any) to evaluate and discard these failed business models. Empirical studies could examine which elements of business model innovation framework are associated with failing business models.

Another way to develop alternative business models is through open innovation. Although different categories of open business models have been identified by researchers (e.g. Frankenberger et al. , 2014 ; Taran et al. , 2015 ; Kortmann and Piller, 2016 ), their effectiveness is yet to be established. Further research is needed to examine when can a firm open and/or close element(s) of the business model innovation framework. Future studies could also examine the characteristics of open and/or close business models.

In responding to disruptive business models, how companies extend their existing business model, introduce additional business model(s) and/or replace their existing business model altogether remains underexplored. Future research is needed to unravel the strategies deployed by firms to extend their existing business models as a response to disruptive business models. In introducing additional business models, Markides (2013) suggested that a company will be presented with several options to manage the two businesses at the same time: create a completely separate business unit, integrate the two business models from the beginning or integrate the second business model after a certain period of time. Finding the balance between separation and integration is of vital importance. Further research could identify which of these choices are most common among successful firms introducing additional business models, how is the balance between integration and separation achieved, and which choice(s) prove more profitable. Moreover, very little is known on how firms replace their existing business model. Longitudinal studies could provide insights into how a firm adopts an alternative model and discard the old business model over time. It may also be worth examining the factors associated with the adoption of business model innovation as a response to disruptive business models. Moreover, new developments in digital technologies such as blockchain, Internet of Things and artificial intelligence are disrupting existing business models and providing firms with alternative avenues to create new business models. Thus far, very little is known on digital business models, the nature of their disruption, and how firms create digital business models and make them disruptive. Future research is needed to fill these important gaps in our knowledge.

6.2 Degrees of business model innovation

Business models can be developed through varying degrees of innovation from an evolutionary process of continuous fine-tuning to a revolutionary process of replacing existing business models. Recent research shows that survival of firms is dependent on the degree of their business model innovation ( Velu, 2015, 2016 ). This review classifies these degrees of innovation into modifying a single element, altering multiple elements simultaneously and/or changing the interactions between elements of the business model innovation framework.

In changing a single element, further research is needed to examine which business model element(s) is (are) associated with business model innovation. It is not clear whether firms intentionally make changes to a single element when carrying out business model innovation or stumble at it when experimenting with new ways of doing things. It may also be worth investigating the entry (or starting) points in the innovation process. There is no consensus in the literature on which element do companies start with when carrying out their business model innovation. While some studies suggest starting with the value proposition ( Eyring et al. , 2011 ; Landau et al. , 2016 ), others suggest starting the innovation process with identifying risks in the value chain ( Girotra and Netessine, 2011 ). Dmitriev et al. (2014) suggested two entry points, namely, value proposition and target customers. In commercialising innovations, the former refers to technology-push innovation while the latter refers to market-pull innovation. Also, it is not clear whether the entry point is the same as the single element associated with changing the business model. Further research can explore the different paths to business model innovation by identifying the entry point and subsequent changes needed to achieve business model innovation.

There is little guidance in the literature on how firms change multiple business model elements simultaneously. Landau et al. (2016) claimed that firms entering emerging markets tend to focus on adjusting specific business model components. It is unclear which elements need configuring, combining and/or integrating to achieve a company’s value proposition. Furthermore, the question of which elements can be “bought” on the market or internally “implemented” and their interplay remains unanswered ( DaSilva and Trkman, 2014 ). Casadesus-Masanell and Ricart (2010) argued that “[…] there is (as yet) no agreement as to the distinctive features of superior business models” (p. 196). Further research is needed to explore these distinctive elements of high-performing business models.

In changing the interactions between business model elements, further research is needed to explore how these elements are linked and what interactions’ changes are necessary to achieve business model innovation. Moreover, the question of how firms sequence these elements remains poorly understood. Future research can explore the synergies created over time between these elements. According to Dmitriev et al. (2014) , we need to improve our understanding of the connective mechanisms and dynamics involved in business model development. More work is needed to explore the different modalities of interdependencies among these elements and empirically testing such interdependencies and their effect on business performance ( Sorescu et al. , 2011 ).

It is surprising that the link between business model innovation and organisational performance has rarely been examined. Changing business models has been found to negatively influence business performance even if it is temporary ( McNamara et al. , 2013 ; Visnjic et al. , 2016 ). Contrary to this, evidence show that modifying business models is positively associated with organisational performance ( Cucculelli and Bettinelli, 2015 ). Empirical research is needed to operationalise the various degrees of innovation in business models and examine their link to organisational performance. Longitudinal studies can also be used to explore this association since it may be the case that business model innovation has a negative influence on performance in the short run and that may change subsequently. Moreover, it is not clear whether high-performing firms change their business models or innovation in business models is a result from superior performance ( Sorescu et al. , 2011 ). Further studies are needed to determine the direction of causality. Another link that is worth exploring is business model innovation and social value, which has only been explored in a few studies looking at social business models (e.g. Yunus et al. , 2010 ; Wilson and Post, 2013 ). Further research is needed to examine this link and possibly examine both financial and non-financial business performance.

6.3 Mechanisms of business model innovation

Although we know more about how firms define value proposition, create and capture value ( Landau et al. , 2016 ; Velu and Jacob, 2014 ), what remains as a blind spot is the mechanism of business model innovation. This is due to the fact that much of the literature seems to focus on value creation. To better understand the various mechanisms of business model innovation, future studies must integrate value proposition, value creation and value capture elements. Empirical studies could use the business model innovation framework to examine the various mechanisms of business model innovation. Also, the literature lacks the integration of internal and external perspectives of business model innovation. Very few studies look at the external drivers of business model innovation and the associated internal changes. The external drivers are referred to as “emerging changes”, which are usually beyond manager’s control ( Demil and Lecocq, 2010 ). Inconclusive findings exist as to how firms develop innovative business models in response to changes in the external environment. Future studies could examine the external factors associated with the changes in the business model innovation framework. Active and reactive responses need to be explored not only to understand the external influences, but also what business model changes are necessary for such responses. A better understanding of the mechanisms of business model innovation can be achieved by not only exploring the external drivers, but also linking them to specific internal changes. Although earlier contributions linking studies to established theories such as the resource-based view, transaction cost economics, activity systems perspective, dynamic capabilities and practice theory have proven to be vital in advancing the literature, developing a theory that elaborates on the antecedents, consequences and different facets of business model innovation is still needed ( Sorescu et al. , 2011 ). Theory can be advanced by depicting the mechanisms of business model innovation through the integration of both internal and external perspectives. Also, we call for more empirical work to uncover these mechanisms and provide managers with the necessary insights to carry out business model innovation.

7. Conclusions

The aim of this review was to explore how firms approach business model innovation. The current literature suggests that business model innovation approaches can either be evolutionary or revolutionary. However, the evidence reviewed points to a more complex picture beyond the simple binary approach, in that, firms can explore alternative business models through experimentation, open and disruptive innovations. Moreover, the evidence highlights further complexity to these approaches as we find that they are in fact a spectrum of various degrees of innovation ranging from modifying a single element, altering multiple elements simultaneously, to changing the interactions between elements of the business model innovation framework. This framework was developed as a navigation map for managers and researchers interested in how to change existing business models. It highlights the key areas of innovation, namely, value proposition, operational value, human capital and financial value. Researchers interested in this area can explore and examine the different paths firms can undertake to change their business models. Although this review pinpoints the different avenues for firm to undertake business model innovation, the mechanisms by which firms can change their business models and the external factors associated with such change remain underexplored.

The evolution of business model literature (pre-2000 to 2016)

Business model innovation framework

Previous reviews of business model literature

Reviewed papers and their subject fields

Source of our sample

Business model innovation areas and elements

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Weill , P. , Malone , T.W. and Apel , T.G. ( 2011 ), “ The business models investors prefer ”, MIT Sloan Management Review , Vol. 52 No. 4 , pp. 17 - 19 .

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• v.9(6); 2023 Jun
• PMC10220359

Business model innovation and firm performance: Evidence from manufacturing SMEs

Natnael salfore.

a University of Gondar, Gondar, Ethiopia

Matiwos Ensermu

b Addis Ababa University, Addis Ababa, Ethiopia

Zerihun Kinde

Associated data.

Data included in article/supp. material/referenced in article.

In the current volatile business environment, companies are obliged to search for new ways of doing business to remain competitive. Accordingly, firms innovate their business model as it became a promising strategy to achieve sustainable outcomes. However, there is still a need for empirical studies that examine the relationship between business model innovation (BMI) and the performance of small and medium-sized enterprises (SMEs). In this study, we aimed to investigate this relationship by collecting data from 264 manufacturing SMEs through structured questionnaires. We employed partial least square structural equation modeling (PLS-SEM) to analyze the collected data and test the hypotheses. The results indicated that changes in any component of the business model, namely value creation, value proposition, or value capture, had a positive and significant relationship with the performance of manufacturing SMEs. Therefore, by innovating their business models, firms can create more value for their customers while capturing value for themselves. In conclusion, increasing use value or decreasing exchange value with customers will help firms create more value and surpass competitors in the marketplace, while also allowing them to capture more value for themselves.

1. Introduction

In the current volatile environment, companies are obliged to rethink and search for unique strategies and innovative ways of doing business to improve their performance [ 1 ] and remain competitive [ 2 ]. For this purpose, firms often make significant efforts to innovate their products and processes to increase their revenue and maintain and/or improve their profit level [ 3 ].

The Organization for Economic Co-operation and Development (OECD) defines innovation broadly as “the introduction of a new or significantly improved product (good or service) or process, new marketing method or a new organizational method in business practice, workplace organization, or external relations” [ 4 ]. However, innovations in products and processes are insufficient to compete in the current market where technology development, globalization, ease of access to information, and development in the world economy are high [ 5 , 6 ]. In addition, innovations to improve products and processes are often costly and time-consuming, require high investment in research and development, and require new equipment and even entirely new business units whose future pay-back is uncertain [ 3 , 5 ]. For these purposes, most firms are now turning to Business Model Innovation (BMI) as an alternative or even complement to product or process innovation, as BMI goes beyond just innovating products, services, or technology that can be easily replicated [ 3 , 7 ].

The business model, which describes how a firm creates, delivers, and captures value [ 8 ], should be either slightly modified or completely changed in response to the changing conditions and strengthened over time as the competitive environment evolves [ 9 ]. Thus, BMI is defined as “the conscious change of an existing business model or the creation of a new business model that better satisfies the needs of the customer than the existing business model” [ 10 , 11 ]. Currently, interest in BMI, and business models in general, is increasing in both practice and research [ 12 , 13 ]. In a global study conducted by the Economic Intelligence Unit (EIU), the majority of senior managers stated they prefer to innovate business models over product and process innovations [ 3 ]. As such, BMI has recently received great attention from scholars and academicians in various fields of study [ 13 , 14 ].

However, empirical research linking BMI to other factors in firms is still scarce and dispersed across different disciplines [ 12 , 15 ]. Most previous studies have primarily focused on defining and explaining the concept and differentiating it from other management concepts [ 11 , 16 ]. Furthermore, empirical studies in this area have yielded conflicting results. While some studies have found a positive relationship between BMI and firm performance [ 12 , 17 ], others have found a negative [ 18 , 19 ], or non-significant relationship between the two [ 20 ]. Despite limited research indicating the relationship between BMI and firm performance, the question of whether changing the business model results in a change in firm performance remains unanswered [ 12 , 13 ]. Moreover, answering this question is very important for small and medium-sized enterprises (SMEs) engaged in manufacturing, as they have fewer resources and capabilities than large firms [ 17 ]. Therefore, given the importance of SMEs in the global economy, empirical research is needed to ascertain whether BMI activity in manufacturing SMEs is associated with improved performance [ 12 , 17 ]. This could be achieved through large-scale empirical investigations of the relationship between BMI and firm performance, utilizing statistical methodologies that ensure greater generalizability of results, as recommended by scholars [ 11 , 21 ].

Therefore, the objective of the current study is to empirically investigate the relationship between BMI and the performance of manufacturing SMEs. By doing so, first, the study broadens the scope of business model research, which has previously focused on defining and differentiating the concept from other concepts and identifying its drivers and antecedents [ 8 , 12 , 16 ]. Second, previous studies in the area have relied on qualitative data and are characterized by an explorative approach to gain a first-hand understanding of the concept [ 11 ]. Furthermore, some empirical studies have made use of secondary data collected for other purposes [ 12 , 22 ]. In contrast, the current study utilizes statistical methods and quantitative data analysis, thereby providing methodological contributions to the existing literature.

The subsequent sections of this paper are organized as follows. Section 2 provides a review of the empirical literature and hypothesis development. Section 3 outlines the methods and materials used in this study. Section 4 presents the results, while Section 5 discusses the implications of the findings with concluding remarks. Finally, Section 6 discusses the implications, limitations, and future research directions.

2. Empirical review and hypothesis development

2.1. business model and business model innovation.

The concept of business model has gained widespread acceptance in corporate practice and high visibility in strategic and entrepreneurship research [ 16 , 23 ]. There is no single widely accepted definition of a business model because the literature develops in silos based on the interests of respective researchers [ 5 , 16 ]. Amit and Zott [ 24 ] define the business model as “the content, structure, and governance of transactions designed so as to create value through the exploitation of business opportunities.” Chesbrough and Rosenbloom [ 25 ] describe the same as “the heuristic logic that connects technical potential with the realization of economic value.” While the concept and definition of a business model differ, various scholars have conceptualized it based on its basic dimensions and agree that its main purpose is to create and deliver value to customers and enterprises themselves [ 6 , 8 ]. In this study, we adopt Teece’s [ 8 ] definition of a business model, which portrays it as “the design or architecture of the value creation, delivery, and capture mechanisms of an organization.”

The first basic element of the business model, value creation, refers to the tasks a firm performs to provide an offer to customers by using its resources and capabilities [ 14 ]. It also indicates the firm’s ability to acquire resources, such as land, capital, and labor, and transform them into products and services [ 6 ]. Value can be created by either increasing the customers' willingness to pay or decreasing the suppliers' and partners' opportunity costs [ 26 ]. On the other hand, the value proposition dimension indicates the company’s bundle of products and services that are of value to customers and the way in which they are offered [ 6 , 14 ]. It also explains how a particular firm differentiates itself from its competitors and the reason why customers buy from that firm rather than others [ 6 ]. Finally, the value capture dimension defines how value offerings are converted into revenue streams and then captured as profits by firms [ 14 ]. According to Chesbrough [ 5 ], the value capture dimension is equally critical to a firm’s success because a company that cannot profit from some of its activities cannot sustain those activities over time.

The business model requires constant vigilance because it cannot be static and last forever by efficiently unlocking, capturing, and redistributing the value added by organizations [ 8 , 15 ]. As a result, it must be either slightly modified or completely changed in response to the changing environmental conditions and strengthened over time [ 9 ]. Thus, BMI is defined as “the discovery of new or conscious change of enterprise’s existing value creation, delivery, and/or capture mechanism that better satisfies customer needs than the existing business model” [ 8 , 10 ].

The concept of a business model is fundamentally linked to technological innovation. For this purpose, different scholars have put forth various perspectives on the interaction between business models and technology. While some argue that developments in technology facilitate new business models [ 5 , 13 , 27 ], others argue that the emergence of business models pushed for the development of new technologies [ 9 ]. However, they all stated unequivocally that innovation in the business model is distinct from innovation in technology. According to Baden-Fuller and Haefliger [ 27 ], the business model is essentially separable from technological innovation, even though there exists a fundamental linkage between the two. In addition, Chesbrough [ 5 ] clearly indicated that innovation in the business model is not the same as innovation in technology. In his words, “better business model often will beat a better technology.” In a later study, Chesbrough [ 28 ] pointed out that companies commercialize their technologies through business models. Otherwise, the economic value of technology remains latent until firms introduce it through a business model. Therefore, firms should innovate their business models in order to reap the outcomes of technological innovations.

2.2. Firm performance

Currently, firm performance has become a relevant concept and ultimate outcome variable in any management research [ 29 ]. Despite its prevalence in the academic literature, there is less consensus about its definition and measurement among researchers [ 30 ]. After the seminal work of Venkatraman and Ramanujam [ 31 ], scholars have started to recognize and measure firm performance as a multidimensional construct [ 29 , 32 ]. Nevertheless, while acknowledging its multidimensionality, several other researchers measure firm performance as unidimensional [ 32 ].

One can measure firm performance objectively (through accounting and financial measures) or subjectively (via survey-based self-reports) [ 30 ]. While objective measures are preferable over subjective measures, objective indicators on the performance of SMEs are difficult to collect [ 30 , 32 ] and, in some cases, impossible to collect [ 31 ]. Singh et al. [ 30 ] also indicated that obtaining consistent and comparable data in the objective measures of performance for the entire sample under investigation is extremely difficult. Furthermore, most owners are not willing to disclose information regarding objective performance measures [ 33 ]. For these purposes, several studies adopt subjective perception-based measures of firm performance [ 30 , 32 , 33 ]. However, whether objective or subjective measures of performance are used is up to the researcher [ 30 ].

2.3. The relationship between BMI and firm performance

There is increasing consensus among scholars regarding the relationship between BMI and firm performance [ 16 ]. According to Pohle and Chapman [ 34 ], BMI is responsible for a significantly larger improvement in the firm’s performance than innovation in product and process. In addition, the authors stressed that BMI is often positively related to reduced costs and, thus, enhanced profits for manufacturers. Teece [ 8 ] also indicated that a well-designed business model that includes all aspects (components), along with the implementation and refinement of commercially feasible revenue and cost architectures, is critical to enterprise success. Furthermore, Cucculelli and Bettinelli [ 22 ] pointed out that modifying a firm’s business model in an innovative way is positively associated with venture performance. In the same way, scholars who conceptualized business models based on their components reported a positive relationship with firm performance. For instance, Clauss et al. [ 35 ] found that innovation in value creation increases firm performance by delivering greater economic results. Similarly, a study by Chen et al. [ 17 ] in manufacturing SMEs indicated that value creation innovation is positively and significantly related to SMEs' performance. Therefore,

Value creation innovation is positively related to the performance of manufacturing SMEs.

Innovation in the value proposition helps firms to extend their product and service portfolios and address new market needs, which in turn is highly associated with their performance [ 35 ]. According to Clauss et al. [ 35 ], a change in value proposition will result in a change in customer offerings, which includes product/service, target markets, and delivery channels. Clauss et al. [ 36 ] also indicated that value proposition innovation has a positive relationship with firm performance. Furthermore, Chen et al. [ 17 ] found that value proposition innovation is positively and significantly associated with manufacturing SMEs' performance. Therefore,

Value proposition innovation is positively associated with the performance of manufacturing SMEs.

Renewing the value capture mechanism can help firms replace less profitable revenue sources and improve their potential profits [ 26 ]. Casadesus-Masanell and Zhu [ 10 ] also indicated that innovations in value capture help firms to realize new revenue streams in addition to existing ones, or to substitute less profitable ones compared to other revenue capture mechanisms, which improves the prospects for future revenue. The authors also emphasized that value capture innovation can reduce inefficiencies and, as a result, improve firms' performance. Therefore,

Value capture innovation is positively related to the performance of manufacturing SMEs.

The overall structure of the relationship between the study variables and firm performance is indicated in Fig. 1 .

Conceptual framework.

3. Methods and materials

3.1. sample and data.

The aim of this study is to investigate the relationship between BMI and manufacturing SMEs' performance. To achieve this, we used primary data collected from SMEs located in Addis Ababa, Ethiopia. At the time of survey (between May and October 2022), there were 1201 small and 602 medium-sized manufacturing enterprises. We proportionally and randomly selected 318 manufacturing SMEs to include in the study sample to ensure that each sector received fair representation. We excluded startups from the study as they don’t have a fully developed business model and lack adequate capital to move on to the next phase [ 33 ]. We collected 276 questionnaires, with eight having limited data. Therefore, we analyzed 264 correct responses. Out of correct responses, 173 were small and 91 were medium-sized enterprises. In Ethiopia, small enterprises have a total capital ranging from Birr 100,001 † to Birr 1,500,000 and employ 6 to 30 workers, including the owner, family members, and other employees. Medium enterprises have a total capital (excluding building) ranging from Birr 1,500,001 to Birr 20,000,000 and employ 31 to 100 workers, including the owner, family members, and other employees (Federal Urban Job Creation and Food Security Agency [FUJCFSA], 2019). The data also showed that 96 enterprises were engaged in wood and steel work, 91 in textile and leather manufacturing, 48 in agro-processing, and the remaining 29 were engaged in the manufacturing of chemicals and industrial inputs. The classification of manufacturing SMEs into sub-sectors in this study was based on the Ethiopian government’s classification system.

We collected data from owners/managers of SMEs using structured questionnaires. Before collecting data, we obtained informed consent from all participants involved in our research. The consent form outlined the purpose of the study and the fact that all data collected would be kept confidential and used solely for academic purposes. We administered the questionnaire on-site to owners/managers of each enterprise. The questionnaire had four sections: background information, BMI, environmental dynamism, and firm performance. The background information section included gender, the respondents' role in the enterprise, educational level, enterprise age, manufacturing sub-sector, and the number of employees. We collected BMI data using an instrument developed by Clauss [ 14 ]. The instrument has 30 Likert-scaled items in which 10 items measure value creation innovation, 12 measure value proposition innovation, and the remaining 8 measure value capture innovation (see Appendix 1 ). Each item ranges from a scale of 1 (strongly disagree) to 5 (strongly agree). The first-order constructs in the measurement scale consist of new capabilities, which can be developed through training and knowledge integration; new technology, which is related to equipment used to carry out BMI; new processes, which are concerned with how activities are connected; new partnerships, which represent external resources available to the firm; new offerings, which justify the firm’s product offerings; new markets, which identify the customer group in which the product is offered; new channels, which are concerned with the delivery of value to customers; new relationships, which indicate the firm’s ability to build or establish a relationship with customers; new revenue models, which are concerned with transforming revenue models to encourage customers to pay for the value proposition; and new cost structures, which are related to the direct and indirect costs of running the business [ 14 ]. We collected data on performance of SMEs by using financial and marketing indicators such as sales growth, profit growth, market share, speed to market, net income, and return on investment, as recommended by Venkatraman and Ramanujam [ 31 ], and we measured them using a Likert-type scale, with response options ranging from 1 (strongly disagree) to 5 (strongly agree).

3.2. Measurement of variables

The study primarily includes two variables: BMI and firm performance, which are predictor and outcome variables, respectively. BMI is concerned with changes in one or more components of the business model, such as value creation, value proposition, and value capture [ 14 ]. Firm performance encompasses both financial and non-financial measures [ 31 ].

Previous studies have used different measurement scales to measure BMI. For instance, Velu [ 37 ] used diversification/product launch and external funding as two indicators of BMI while Anwar [ 38 ] utilized six items developed by Karimi and Walter that focused on different aspects of a firm’s innovativeness, such as product and service, delivery, process, and structure. On the other hand, Clauss [ 14 ] developed and validated a BMI measurement scale, which Bouwman et al. [ 12 ] identified as a valuable contribution to measuring BMI. As a result, we measured BMI by assessing the changes introduced in the three dimensions of the business model: value creation, value proposition, and value capture. To capture the multi-dimensional nature of BMI, we used a reflective-formative measurement model and estimated hierarchical latent variables: value creation innovation, value proposition innovation, and value capture innovation using a two-stage approach in PLS-SEM, which has advantages over other methods [ 39 ].

We measured firm performance using Venkatraman and Ramanujam’s performance measurement scale [ 31 ]. Initially, the authors identified three dimensions of business performance, namely financial, operational, and market performance. However, they later combined the market and financial performance dimensions into one, resulting in a two-dimensional framework of business performance—financial and operational—that has become increasingly prevalent in recent empirical research [ 12 , 32 , 33 ]. We used subjective data to measure the performance of manufacturing SMEs since objective data on their performance is difficult to obtain as they do not publish their financial results [ 32 ]. Furthermore, owners are typically unwilling to voluntarily disclose their business’s financial data to outsiders [ 30 , 33 ]. Therefore, we ask owners/managers of enterprises to rate their company’s performance against primary competitors within the industry during the last two years.

Internal and external conditions of a firm may affect the relationship between BMI and firm performance [ 26 ]. To reduce potential confounding effects caused by omitted variables, we incorporated three control variables into our model: firm age, size, and environmental dynamism. Semrau et al. [ 40 ] also recommend controlling the firm’s age and size for better results. We measured firm age based on the logarithm of the number of years since the establishment, firm size with the logarithm of the number of employees, and we measured environmental dynamism by a measurement scale developed by Ting et al. [ 41 ], which includes changes in technology; variations in customer preferences, product demand, and competitors; and fluctuations in the supply of materials.

From the total of 318 questionnaires distributed to manufacturing SMEs, 276 have been returned, resulting in an 86.8% response rate. We excluded 12 questionnaires from the analysis because eight were incorrectly filled and four were filled by startups that were not part of our study. Therefore, we analyzed 264 correct responses.

4.1. Descriptive results

We used descriptive statistics to summarize the demographics of our sample. Of 264 respondents, 65.2% were male and 34.8% were female. The age distribution of data indicated that 38.3% of the sample were between the ages of 18 and 30, 42.8% were between the ages of 31 and 40, 11.4% were between 41 and 50, and the remaining 7.6% were above 50 years old. Of the respondents who participated in the survey, 76.1% were owners, 19.3% were managers, and the remaining 4.5% were representatives of the owners/managers. The age distribution of firms showed that 46.6% had been in business for 3–8 years, 40.9% for 9–14 years, and the remaining 12.5% for 15 years or more (see Table 1 ).

Background information.

The new capabilities had the highest mean score ( X ‾  = 4.120, S = 0.594) compared to other constructs, followed by the new cost structure ( X ‾  = 4.039, S = 0.605). This indicates that, compared to others, manufacturing SMEs train their employees to help them catch up-to-date knowledge, adapt to changing markets, regularly reflect on price-quantity strategy, seek opportunities to save manufacturing costs, constantly examine market prices and act accordingly, and use opportunities of price differentiation [ 14 ]. The new channels had the lowest mean score ( X ‾  = 2.976, S = 0.745), followed by the new revenue models ( X ‾  = 3.171, S = 0.690), compared with others (see Table 2 ). This indicates that, relative to others, manufacturing SMEs do not regularly change their distribution channels, have not benefited from channels change, have not recently developed new revenue opportunities, do not offer integrated services, do not practice recurring revenue models, and rely heavily on existing revenue sources [ 33 ].

Means, standard deviation, and correlation matrix.

**. Correlation is significant at the 0.01 level (2-tailed).

4.2. Correlation matrix

We used Pearson Correlation to indicate the association between variables. According to Norman [ 42 ], parametric analysis methods can be used with Likert-scaled measurements without fear of “coming to the wrong conclusion.” As shown in Table 2 , all the constructs were positively and significantly correlated with firm performance. We did not observe any multicollinearity issue in our analysis since all the constructs had correlation values below 0.80 [ 43 ].

4.3. Common method bias

Because we collected both independent and dependent data from the same sources at the same time, common method bias (CMB) may threaten the results [ 44 ]. The researchers recommend two methods: procedural and statistical, for mitigating CMB [ 45 , 46 ]. We implemented several procedural remedies to minimize the occurrence of CMB before conducting the survey. We assured respondents that their anonymity would be protected, and we attempted to reduce participants' evaluation apprehension over their responses by informing them that this was just a survey. According to Podsakoff et al. [ 45 ], the measures taken before the experiment can reduce the likelihood and severity of CMB.

Furthermore, we performed Harman’s single-factor method using principal component analysis. According to Tehseen et al. [ 46 ], this method loads all items from each construct into an exploratory factor analysis to see whether one single factor emerges or one general factor accounts for a majority of the covariance among the measures. While some argue that Harman’s single-factor method is inadequate for assessing CMB [ 45 ], other recent studies indicate that it is an easy and meaningful tool to assess CMB [ 46 , 47 ]. The results revealed that the total variance extracted by a single factor for the sample was 39.5% (see Appendix 2 ). Therefore, CMB was not a pervasive issue in this study, as it was below 50% [ 46 ].

4.4. Validity and reliability

We assessed the internal consistency reliability of measurement models using Cronbach’s alpha, which is commonly used to measure internal consistency with a threshold value of 0.70 [ 48 ]. All the values are within the specified threshold. Internal consistency can better be measured by composite reliability than Cronbach’s alpha [ 48 , 49 ]. As a result, we measured composite reliability for our measurement model, and, as indicated in Table 3 , all the values were higher than the threshold value of 0.70. According to Pesämaa et al. [ 47 ], both Cronbach’s alpha and composite reliability should exceed 0.70 to be considered acceptable. Furthermore, rho_A—which is recommended as an accurate and approximate measure of reliability [ 48 ]—was found to be within the acceptable range [ 50 ], as shown in Table 3 .

Assessment of Reflective Measurement Model for first order constructs.

Note: α = Cronbach’s Alpha; rho_A = Reliability Coefficient; CR = Composite Reliability; AVE = Average Value Extracted; VIF = Variance Inflation Factor.

In addition, we assessed the convergent validity of the construct to investigate its ability to effectively explain the variance of its items. According to Ringle et al. [ 49 ], the average variance extracted (AVE) metric can be used to measure the construct’s convergent validity, with values of 0.50 and higher being considered acceptable. Therefore, as shown in Table 3 , all the AVE values in our measurement model were greater than 0.50 and were acceptable. We also examined the model for discriminant validity using the heterotrait-monotrait (HTMT) ratio of correlations. As shown in Table 4 , all the HTMT ratio values in our measurement model were less than or equal to 0.85, which is a strong threshold value [ 51 ].

Discriminant Validity of Constructs using HTMT Ratio.

Note: HTMT = heterotrait-monotrait ratio of correlations.

Furthermore, we tested the normality of the data using the Shapiro-Wilk test of normality and the results indicated that none of the constructs followed a normal distribution, with p-values less than 0.05 (see Appendix 3 ). According to Field [ 52 ], if the significance test is greater than 0.05, the distribution is normal; if it is less than 0.05, it deviates significantly from a normal distribution. However, since PLS-SEM is a non-parametric method that does not require distributional assumptions, the lack of normality did not pose a problem [ 43 , 53 ].

4.5. Path model analysis

We used Partial Least Score Structural Equation Model (PLS-SEM) using SmartPLS v.3 to analyze the data and test the hypothesis. We chose the PLS-SEM approach over the CB-SEM approach for several reasons. First, the PLS-SEM approach allows the estimation of very complex models with many constructs and subsequent analysis of latent variables [ 48 ]. Second, it places fewer restrictions on data distribution and normality [ 48 , 53 ]. And third, it can extensively estimate interactions among latent predictor variable indicators [ 43 , 48 ].

4.5.1. Measurement model assessment

The measurement model was used to establish the relationship between the constructs and the measured variables. According to Hair et al. [ 48 ], the first step in assessing a reflective measurement model is to examine indicator loadings. Indicator loadings above 0.708 are recommended since they indicate that the construct explains more than 50% of the indicator’s variance [ 54 ]. Our assessment of the indicator loadings for the reflective measurement models showed that all outer loadings had values greater than the recommended threshold of 0.708 [ 48 ], as shown in Table 3 . Additionally, we evaluated the reliability and validity of construct measures as per Ali et al. [ 53 ]. The assessment indicated that all results met the specified criteria and were acceptable.

4.5.2. Structural model assessment

After confirming that the measurement model assessment met the relevant criteria, we proceeded to evaluate the structural model results. We assessed the structural model in PLS-SEM for statistical relevance using the coefficient of determination (R 2 ), effect size (f 2 ), Stone-Geisser’s predictive relevance (Q 2 ) through the blindfolding procedure, and the statistical significance, after checking collinearity among variables. As shown in Table 5 , the value of R 2 was 0.448, indicating that all variables together predict 44.8% of the variation in the performance of manufacturing SMEs. This result is considered satisfactory since Falk and Miller [ 55 ] suggest an R 2 value of 0.1 or higher as adequate for explaining the variance of a particular endogenous construct. The values of f 2 , presented in Table 6 , indicated a medium effect size [ 49 ]. Moreover, the predictive relevance, Q 2 , which measures whether the model has predictive relevance or not [ 48 ], was good because its value was greater than 0. The path coefficients were acceptable because they fell between −1 and +1 and were statistically significant at the 5% level [ 54 ]. We further assessed the model fit using the Standardized Root Mean Square Residual (SRMR), a measure of the difference between the observed correlation and model-implied correlation matrix, and determined that the estimated SRMR value, 0.056, was below the recommended threshold of 0.08 [ 56 ]. In general, our results suggested that the structural model was statistically significant and had a satisfactory fit to the data.

Results of R 2 and Q. 2 .

Note: R 2  = Coefficient of determination, Q 2  = Predictive Relevance, SSO = Sum of Squares Observations, SSE = Sum of Squares Errors, SRMR = Standardized Root Mean Square Residual.

Path coefficients and their significance value.

Note: CVRI = Value Creation Innovation, the process of changing the tasks that a firm performs to provide an offer to customers; VPRI = Value Proposition Innovation, the process of innovating the company’s bundle of products and services and the way in which they are offered to customers; VCAI = Value Capture Innovation, the process of innovating how value offerings are converted into revenue streams and then captured as profits by firms.

4.5.3. Structural model analysis

The structural model displays the relationship between constructs and dependent variables. Hair et al. [ 48 ] recommend examining the collinearity before assessing structural relationships to ensure unbiased regression results. Therefore, we first examined collinearity using VIF values. The results, as presented in Table 3 , showed that all VIF values were less than 3, indicating that our model was free from any possible collinearity issues [ 57 ].

We tested the effect of control variables on endogenous constructs before analyzing our hypothesized variables by including only control variables in the model. While environmental dynamism had a significant effect on company performance, the age and size of a company had no significant effect. The value of adjusted R 2 improved after including control variables in our model.

All the study variables, namely value creation innovation (VCRI), value proposition innovation (VPRI), and value capture innovation (VCAI), were found to have a significant relationship with firm performance. Specifically, H1 evaluated whether VCRI had a significant relationship with SMEs' performance. The results showed that VCRI had a positive and significant relationship with SMEs' performance, supporting H1. These results indicate that when there is a one-unit standard deviation increase in VCRI, manufacturing SMEs' performance also increases by 0.291 standard deviation units. H2 evaluated whether VPRI had a significant association with manufacturing SMEs' performance. The results revealed that VPRI had a positive and significant relationship with the performance of manufacturing SMEs. Therefore, H2 was supported, and a one-unit standard deviation increase in VPRI would lead to 0.216 standard deviation unit increase in the performance of manufacturing SMEs. Finally, H3 tested whether VCAI had a significant relationship with SMEs' performance. The results revealed that VCAI had a significant and positive relationship with SMEs' performance. As a result, H3 was supported, and a one-unit standard deviation change in VCAI would result in 0.255 standard deviation unit change in manufacturing SMEs' performance (see Table 6 ).

5. Discussion and conclusion

The purpose of this study was to investigate the relationship between BMI and manufacturing SMEs' performance. We conceptualized BMI as any changes made to the dimensions of the business model—value creation, value proposition, and value capture.

The structural model indicated that the first hypothesis stating that “value creation innovation is positively related to the performance of manufacturing SMEs” is supported as it has a positive and significant path coefficient. This finding is consistent with a previous study by Chen et al. [ 17 ]. According to Priem [ 58 ], firms can create value by either increasing the use value or decreasing the exchange value from the customer’s perspective, as both increase customer surplus. In addition, Al-Debei and Avison [ 59 ] indicate that innovation in the value creation dimension can be achieved through resource configuration, which reflects a firm’s ability to integrate various assets in a way that offers a valuable bundle of products and services.

The second hypothesis stating that “value proposition innovation is positively associated with the performance of manufacturing SMEs” is supported because it has a positive and significant path coefficient. This finding is consistent with the previous research by Clauss et al. [ 35 ], who found a positive association between value proposition innovation and firm performance. Thus, innovation in value proposition helps firms to attract and retain a large portion of their customer base [ 59 ].

Finally, the third hypothesis stating that “value capture innovation is positively related to the performance of manufacturing SMEs” is also supported as it has a positive and significant path coefficient. This result is consistent with the findings of studies by Zott and Amit [ 26 ] and Casadesus-Masanell and Zhu [ 10 ], both of which indicated that creating new revenue model benefits firms. However, this finding contradicts the findings of Chen et al. [ 17 ] and Clauss et al. [ 35 ], who both found that value capture innovation is negatively related to firm performance. But the study by Chen et al. [ 17 ] indicated that the relationship between value capture innovation and the growth of SMEs is insignificant. To capture value from their innovations, enterprises need to figure out a way to surpass their competitors in the marketplace [ 25 ].

Compared with larger firms, SMEs typically have limited financial and non-financial resources, smaller or non-existent R&D facilities, less technical capabilities, difficulty in hiring multi-skilled labor, and a less structured approach to innovation [ 12 , 17 ]. Despite these limitations, if SMEs can find a way to innovate their business model, they can compensate for these difficulties by relying on their strengths that come from their size, such as change receptiveness, less bureaucratic procedures, flexible structures, and high adaptability [ 60 ]. To innovate their business model, SMEs can modify a single business element, such as value creation, value proposition, or value capture innovation; change two or more components simultaneously; or change the interaction between elements of the business model [ 3 ].

6. Implications, limitations, and future research directions

Our study contributes to the business model and BMI literature in several ways. First, we provide theoretical and empirical reasoning for considering BMI as a configuration of different dimensions. Most previous researchers have considered BMI as an aggregate construct [ 12 , 17 ], viewed BMI as the sole change in the value creation dimension of the business model [ 12 ], and relied only on proxy measures to measure BMI [ 11 ]. In contrast, our study distinguishes three dimensions of the business model, namely value creation, value proposition, and value capture, and investigates the relationship between change in each of the dimensions and SMEs' performance. Second, this study offers more conclusive and rigorous evidence of the relationship between BMI and manufacturing SMEs' performance. Prior researches in the area have been criticized for being inconclusive, lacking rigor, and not being empirical [ 17 ].

Furthermore, our study has significant implications for the managerial practices of manufacturing SMEs. Specifically, the study suggests that focusing on customer value propositions instead of just products or services, investing in innovative resources, creating effective partnerships with other strategic parties in the business environment, embracing a distinct market positioning unlike competitors, enticing customers with additional incentives through pricing strategies, and monitoring changing trends in the business environment can help firms obtain early insights into industry developments.

Despite its significant contribution to both theory and practice, the study is not free from some limitations. First, the study relied on subjective measures of firm performance instead of objective ones, which are generally preferred. However, obtaining objective measures is difficult or even impossible for some measures [ 30 , 31 ]. Furthermore, SMEs are not legally required to publish their financial performance, and even if they do, the data may be biased due to the lack of an appropriate auditing system [ 32 , 33 ]. As a result, we relied on subjective data to measure SMEs' performance. Second, our study depended on cross-sectional data obtained from a single informant at the same time for predictor and outcome variables. This may result in common method bias [ 44 ]. To minimize this effect, we first used some procedural remedies and then we checked for the availability of this potential problem with Harman’s single-factor test using principal component analysis. The result indicated that common method bias did not pose a problem because its value was below 50% [ 61 ]. However, future researchers may use a longitudinal research design or collect predictor and outcome variables at different time periods. Third, we physically provided questionnaires to owners/managers of each enterprise. Even though this method of data collection may result in a high response rate, it may compromise the results due to a lack of anonymity. Therefore, future studies ought to use a survey instrument that best preserves the anonymity of respondents. Fourth, there may be circumstances in which the relationship between BMI and firm performance can be strengthened by including contingency variables [ 13 , 33 ]. Therefore, we suggest that future research include the mediating or moderating factors when studying the link between BMI and firm performance.

Author contribution statement

Natnael Salfore: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Matiwos Ensermu: Zerihun Kinde: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Data availability statement

Declaration of competing interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Acknowledgments

We would like to thank Salale University and the University of Gondar for supporting the corresponding author, a Ph.D. scholar. We also thank the Bureau of Labor, Enterprise, and Industry Development of Addis Ababa City and subsequent coordinators at the sub-city level for providing the required information for this study. Furthermore, we thank Dr. Daksa, Mr. Dana, and Mr. Metiku for proofreading our manuscript. Finally, we would like to express our heartfelt gratitude to the owners and managers of each enterprise for taking the time to respond to our survey.

† 1 USD = 54.35 Birr as of April 24, 2023.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e16384 .

Appendices. 

Measurement Scale

1) Business Model Innovation (Clauss, 2017).

A) Value Creation Innovation.

New capability:

• - Our employees constantly receive training in order to develop new competencies.
• - Relative to our direct competitors, our employees have very up-to-date knowledge and capabilities.
• - We constantly reflect on which new competencies need to be established in order to adapt to changing market requirements.

New Technology:

• - We keep the technical resources of our company up-to-date.
• - Relative to our competitors our technical equipment is very innovative.
• - We regularly utilize new technical opportunities in order to extend our product and service portfolio.

New Partnership:

• - We are constantly searching for new collaboration partners
• - We regularly utilize opportunities that arise from the integration of new partners into our processes
• - We regularly evaluate the potential benefits of outsourcing
• - New collaboration partners regularly help us to further develop our business model.

New relationships:

• - We were recently able to significantly improve our internal processes.
• - We utilize innovative procedures and processes during the manufacturing of our products
• - Our existing processes are regularly assessed and significantly changed if needed

B) Value Proposition Innovation.

New offerings:

• - We regularly address new, unmet customer needs
• - Our products or services are very innovative in relation to our competitors
• - Our products or services regularly solve customer needs, which were not solved by competitors

New markets:

• - We regularly take opportunities that arise in new or growing markets
• - We regularly address new, un-served market segments
• - We are constantly seeking new customer segments and markets for our products and services

New channels:

• - We regularly utilize new distribution channels for our products and services
• - Constant changes in our channels have led to improved efficiency of our channel functions
• - We consistently change our portfolio of distribution channels

New Relations:

• - We try to increase customer retention through new service offerings
• - We emphasize innovative/modern actions to increase customer retention
• - We recently took many actions in order to strengthen customer relationships

C) Value capture Innovation.

New revenue model:

• - We recently developed new revenue opportunities (e.g. additional sales, cross-selling)
• - We increasingly offer integrated services (e.g. maintenance contracts) in order to realize long-term financial returns
• - We recently complemented or replaced one-time transaction revenues with long-term recurring revenue models (e.g. Leasing).
• - We do not rely on the durability of our existing revenue sources.

New cost structure:

• - We regularly reflect on our price-quantity strategy.
• - We actively seek opportunities to save manufacturing costs
• - Our production costs are constantly examined and necessarily amended according to market prices.
• - We regularly utilize opportunities that arise through price differentiation

2) Firm Performance (Venkatraman and Ramanujam, 1986; Pucci et al., 2017; Latifi et al., 2021).

• - Relative to our competitors, our sales growth was much better
• - Relative to our competitors, our profit growth was much better
• - Relative to our competitors, our market share was much better
• - Relative to our competitors, our speed to market was much better
• - Relative to our competitors, our net income was much better
• - Relative to our competitors, our return on investment was much better

Harman’s Single-Factor

Extraction Method: Principal Component Analysis.

Normality Test

a. Lilliefors Significance Correction.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

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Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Empirical Research: Business Research

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Empirical Research

• Reports research based on experience, observation or experiment
• Tests a hypothesis against real data
• May use quantitative research methods that generate numerical data to establish causal relationships between variables 
• May use qualitative research methods that analyze behaviors, beliefs, feelings, or values 

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For empirical research studies, especially targeting Business topics, you could explore the database options below.

In each database, select the box for  Peer Reviewed  or  Scholarly Peer Reviewed Journals  to help filter the results to the type of publication that would have empirical research.

• Business Source Ultimate Offering a comprehensive full-text coverage plus indexing and abstracts for the most important scholarly business journals, dating back as far as 1886.
• ABI/INFORM Collection 2,700 scholarly business journals and thousands of worldwide business periodicals.
• Academic Search Ultimate Scholarly full-text peer-reviewed academic journals plus thousands of periodicals, trade publications and magazines supporting all subjects and programs.

Sample Search Strategy

 In the databases you can search phrases in quotation marks like "qualitative research", "qualitative research", "empirical research", etc.

If for example you are seeking qualitative research studies in business, you could try searching in the Business Source Ultimate  database for: "empirical research" AND business. The peer reviewed box would need to be selected to filter the results to review only the peer reviewed scholarly/academic journals.

For healthcare related topics you could try these databases:

• Public Health This database delivers core public health literature from over 8,000 publications. With journals, dissertations, videos, news, trade publications, reports, and more.
• MEDLINE Plus This resource offers medical professionals and researchers access to unmatched evidence-based and peer-reviewed full-text content from more of the top biomedical journals.

As you move through your research, if you have questions on selecting a topic or verification of a source, please feel free to connect with the course professor. The course professor is the resource for clarification of course content and assignment expectations.

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• Published: 14 May 2024

Linking organisational learning, performance, and sustainable performance in universities: an empirical study in Europe

• Roba Elbawab   ORCID: orcid.org/0000-0003-2152-229X 1 , 2  

Humanities and Social Sciences Communications volume  11 , Article number:  626 ( 2024 ) Cite this article

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Universities are facing changes that could be adapted by learning. Organisational learning helps universities in attaining better organisational and sustainable performance. The study aims to combine and explore how organisational learning culture enables organisational learning to contribute to better organisational performance and better sustainable performance, following the natural resource-based view and organisational learning theory. The study examines the relationship between organisational learning culture, organisational learning, organisational performance, and sustainable performance in the university context from university teachers. The author collected 221 surveys from public university teachers in Europe to test the model. The results indicate a positive relationship between organisational learning culture and organisational learning. In addition to that, the positive relationship between organisational learning and organisational performance is indicated. Moreover, the results indicate a positive relationship between organisational learning and sustainable performance. The results also show that the organisational learning process mediates organisational learning culture and university performance. The study addresses a gap in the scarce studies in the university context for organisational learning and sustainable performance. Finally, this study reproduces an organisational model that has been adapted for universities.

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Introduction.

Universities are facing different types of change, including digitalisation, sustainability, entrepreneurship, and innovation (Leal Filho et al. 2018 ; Pocol et al. 2022 ); it is the universities’ obligation to cope with this change (Medne et al. 2022 ; James et al. 1993 ). One of the proven ways that help universities adapt to change and increase their performance is learning, more specifically, organisational learning (Kezar and Holcombe 2019 ). In fact, organisational learning practices and processes can facilitate change and enhance organisations (Argyris and Schön 1996 ; Fiol and Lyles 1985 ; Garvin 1993 ; Huber 1991 ).

The topic of organisational learning has been discussed since the early nineties when the foundations of organisational learning were further developed during this era (Castaneda et al. 2018 ). Researchers called for more research to understand organisational learning. Organisations learn when there is information processing that leads to a change in the behaviour and the acquisition of knowledge, skills, and abilities (Kezar and Holcombe 2019 ; Flores et al. 2012 ; Jyothibabu et al. 2010 ; Jiménez Jiménez and SanzValle 2006 ; Slater and Narver 1995 ; Huber, 1991 ). Organisational learning researchers have extended their research to identify the organisational learning predictors to include organisational learning culture (Flores et al. 2012 ). Organisational learning has been discussed in several industries, but it is considered scarce in the university context (Abu‐Tineh 2011 ; Voolaid and Ehrlich 2017 ). Previous research focused on organisational learning capabilities and behaviours. However, the author will focus on organisational learning processes in this study. The organisational learning processes affect organisational performance (Bontis et al. 2002 ; Crossan and Bapuji 2003 ; Kontoghiorghes et al. 2005 ; Sun et al. 2008 ; Jyothibabu et al. 2010 ), as well as sustainability and sustainable performance (Iqbal and Ahmad 2021 ; Kordab et al. 2020 ; Bilan et al. 2020 ). However, this relationship in the university sector is understudied, with a gap in organisational learning literature. Especially since universities are considered a complex type of organisation (Elbawab 2022a ; Bleiklie and Kogan 2007 ). Sustainable development was defined as ‘development which meets the needs of the present, without compromising the ability of future generations to meet their own needs’ by the Brundtland Commission in 1987 (Basiago 1995 ). Sustainable development and learning share many important elements, including “a challenge to mental models, fostering fundamental change, engaging in extensive collaborative activity and, in some cases, revisiting core assumptions about business and its purpose” (Molnar and Mulvihill 2003 , p. 168), therefore several scholars showed the need to understand the relationship between learning in organisations and sustainability (Feeney et al. 2023 ). Sustainability is used in different domains, including economics and education (Pocol et al. 2022 ; Basiago 1995 ). In 2015, The United Nations General Assembly approved the ‘2030 Agenda for Sustainable Development’, which contains a set of measures aiming to balance economic progress and the protection of the environment (Leal Filho et al. 2018 ). The agenda consists of 17 Sustainable Development Goals (SDGs), which, among many other tasks, plan to eradicate poverty and create better health conditions in both developed and developing countries (Leal Filho et al. 2018 ). Sustainability in higher education institutions can be implemented in teaching, research, governance, and outreach (Leal Filho et al. 2023 ; Serafini et al. 2022 ). In fact, higher education’s growth contributes to society’s better sustainable development (Geng et al. 2023 ; Geng et al. 2020a ). Therefore, this is one of the reasons for the need to focus on studying sustainability in higher education. In the research area, sustainability research can be implemented by researchers from various areas who can work independently or collectively on the same project by combining their expertise (Leal Filho et al. 2023 ; Collin 2009 ). Additionally, sustainability can be implemented in research by framing higher education institutions’ research in the direction of the SDGs (Serafini et al. 2022 ). In the teaching area, sustainability can be implemented within the strategies in the curriculum development of promoting sustainability and in planning new courses (Leal Filho et al. 2023 ; Serafini et al. 2022 ). Sustainability in the teaching area can also be developed by modifying the existing curriculum with the SDGs (Leal Filho et al. 2023 ). As for governance, sustainability can be implemented by establishing indicators in rankings that evaluate the performance of higher education institutions concerning compliance with the SDGs. Sustainability within governance can also be implemented by evaluating the level of knowledge, awareness, and attitudes towards the SDGs among academic community members and educators (Serafini et al. 2022 ). Finally, for the outreach, sustainability can be implemented by disseminating SDGs by training the managers and decision-makers in civil society organisations (Serafini et al. 2022 ). In this study, the author will focus on the governance of sustainability in education. The relationship between organisational learning and sustainability has been discussed in several studies. A study developed in 2020 assessed the relationship between organisational learning and sustainable organisational performance (Kordab et al. 2020 ). Another study assessed the relationship between organisational learning and sustainability (Bilan et al. 2020 ). Subsequently, the lack of studies that evaluate organisational learning and sustainability in universities has emerged.

According to the natural resource-based view (NRBV) theory (Hart 1995 ), environmentally friendly resources and capabilities play a key competitive advantage in an organisation (Iqbal and Ahmad 2021 ; Hart 1995 ). The NRBV theory takes the capabilities and competitive advantage thinking one step further, where the theory posits that the organisation’s competitive advantage can only be sustained when the capabilities creating the advantage are supported by resources not easily duplicated by competitors (Hart 1995 ). In this study, the resource is the organisational learning. In organisational learning theory, organisational learning is defined as the change that occurs in the organisation, resulting from knowledge memorised in organisations gathered from experience and changes in behaviour resulting from such knowledge (Argote and Miron-Spektor 2011 ). These experiences and changes in behaviours that happen in the organisation are not easily duplicated by the competitors. To explain organisational learning more thoroughly and to show how organisational learning is a resource that is not easily duplicated, the author will further explain the types of learning that are crucial for the organisational learning theory (Crossan et al. 1999 ). Organisational learning has two types of learning. The first is characterised by improving the existing routines, and the second is characterised by reframing a situation or solving unclear problems (Edmondson 2002 ). Since the existing routines and situations will differ from one organisation to another, thus organisational learning can be considered a resource that is not easy to duplicate. Consequently, environmentally friendly resources should have a relationship with organisational learning. Subsequently, this study explores the relationship between organisational learning and sustainable performance (Environmental performance and social performance). The significance of the study is found at both empirical and theoretical levels. Theoretically, it is found to explore the influence of organisational learning as a process on university performance. Also, the influence of learning on sustainability and the role of learning culture among these relationships. This study adds to the theory of organisational learning and, specifically, how to treat organisational learning as a process. Moreover, another significant aspect of this study is addressing the mediation of the organisational learning process in the relationship between organisational learning culture and university performance, as organisational learning culture is proposed to directly affect higher education institutional performance (Kumar 2005 ). Furthermore, the significance of the study is found in exploring the relationship between organisational learning and sustainable performance in the university context. Finally, it empirically assesses organisational learning and all the relationships in the university’s context.

This study assesses the relationship between organisational learning culture and organisational learning processes. The study will also assess the organisational learning processes with the outcomes, where the relationship between the organisational learning process and university performance is assessed. The study will also assess the relationship between organisational learning processes and sustainable performance. Further, this research assesses the mediation between the organisational learning culture and university performance.

Literature review and hypothesis development

Organisational learning process.

Most scholars agree that organisational learning is known as the change in organisational knowledge, which is acquired through practical experiences (Argote and Miron-Spektor 2011 ), where this knowledge is then translated into the organisation’s knowledge system (Do et al. 2022 ). Organisational learning is defined as “the process by which organisations learn” (Chiva et al. 2007 , p. 224; Domínguez-Escrig et al. 2022 ). Organisational learning focuses not only on intentional learning but also on unintentional learning in the organisation (Huber 1991 ), as organisational learning helps reduce uncertainty (Schönherr et al. 2023 ). Learning can happen intentionally and unintentionally (Huber 1991 ). Intentional learning is the main process for scientists and educators. Researchers often think of it as an intentional process directed at improving effectiveness. In contrast, unintentional learning is proposed as unsystematic learning (Huber 1991 ). Even though previous research has focused on organisational learning as a culture or as an outcome, fewer have discovered organisational learning processes (Pham Thi Bich, Tran Quang 2016 ). Consequently, the author focuses on the organisational learning process due to the scarcity of research in this area. Subsequently, universities’ organisational learning will be better understood (Abu‐Tineh 2011 ) and could be enhanced.

Huber ( 1991 ) suggested that organisational learning includes four processes. The processes are information acquisition, knowledge dissemination, shared interpretation, and organisational memory (Huber 1991 ; Santos-Vijande et al. 2012 ). The relevant organisational learning processes in the university sector proposed by the study (Elbawab 2022b ) are information acquisition and knowledge dissemination. In this research, the author has empirically assessed the organisational learning processes and proposed that the relevant processes are information acquisition and knowledge dissemination. Hence, these are the processes used in this paper. The process of information acquisition is about acquiring information from various sources, either internally or externally (Huber 1991 ; Flores et al. 2012 ). The internal information is gathered from inside the organisation and from the company’s creator or previously acquired experience. As for the external information, it is gathered from the competitors and the marketplace through acknowledging and acquiring the implicit analysis of the actions of the competitors. On other occasions, organisations look for the best practices, and they solve the problems by identifying key tendencies, collecting external information, and comparing their performance with that of their relevant competitors (Santos-Vijande et al. 2012 ).

Knowledge dissemination is a process that takes place through formal settings (e.g., departmental meetings, discussion of future needs, and cross-training) and informal interactions among individuals within the organisation (Kofman and Senge 1993 ). The creation of formal networks and databases encourages communication by guaranteeing both the accuracy and the rapid dissemination of information. These initiatives need more informal exchange mechanisms to complement them so that any tacit knowledge individuals gather is transformed into explicit knowledge. Researchers perceive organisational learning as either an organisational process or an organisational capability. Organisational capability is the organisational and managerial characteristics that facilitate the organisational learning process or allow an organisation to learn (Aragón et al. 2014 ; Chiva et al. 2007 ; Tohidi et al. 2012 ). In the present study, organisational learning is viewed as a process that occurs inside the organisation on an organisational level. Organisational learning as a process focuses on the set of actions that occur in the organisation to help in the learning process. In the university context, researchers have called for more studies to understand the organisational learning process (Abu‐Tineh 2011 ). Voolaid and Ehrlich ( 2017 ) assessed organisational learning in two Estonian universities, but also from a cultural perspective and with the perception of the two universities. The researchers insisted on the scarcity of organisational learning research in higher education institutions and the need for a study that empirically assesses organisational learning in more universities from different countries and regions. Further, more research is needed to understand the multidimensionality from an aggregated perspective.

Organisational learning culture

Organisational learning culture is a general predictor of the organisational learning process. The organisational learning culture is essential for organisational learning (Flores et al. 2012 ). Higher education institutions need to adapt to the competition with new discoveries and ideas proactively. The development of a learning culture could be the key, to help through gathering, organising, sharing, and analysing the knowledge across the institution (Kumar 2005). Previously, several predictors have been mentioned for organisational learning, including knowledge-sharing behaviour (Park and Kim 2018 ), goal orientation (Chadwick and Raver 2012 ), participative decision-making, openness, learning orientation and transformational leadership (Flores et al. 2012 ). Flores et al. ( 2012 ) mentioned that these predictors are part of the culture, whereas organisational learning culture is considered a predictor that should be assessed in relation to organisational learning. Consequently, this study assesses organisational learning culture as a predictor of organisational learning. Pham Thi Bich and Tran Quang ( 2016 ) recommend that more predictors positively influencing organisational learning should be explored.

Organisational culture is a factor that facilitates organisational learning (e.g., Ahmed et al. 1999 ; Campbell and Cairns 1994 ; Conner and Clawson 2004 ; Maccoby 2003 ; Marquardt 1996 ; Marsick and Watkins 2003 ; Pedler et al. 1997 ; Rebelo and Duarte Gomes 2011 ). An organisational learning culture is described as the values, beliefs and assumptions that emphasise creating collective learning in an organisation (Sorakraikitikul and Siengthai 2014 ). Researchers have shown the importance of an organisational learning culture as a culture that creates a supportive environment. This culture enables and influences learning and knowledge sharing at the individual, team, and organisational levels (Kontoghiorghes et al. 2005 ; Marsick and Watkins 2003 ). Despite the importance of organisational learning culture in the literature (e.g., Marquardt 1996 ; Pedler et al. 1997 ), there is still a lack of research explicitly concerning learning culture (Rebelo and Duarte Gomes 2011 ) and its relationship with organisational learning. Also, a study developed by Wahda ( 2017 ) has agreed with the scarcity of studies that assess organisational learning culture in higher education institutions. In Wahda’s ( 2017 ) study, the results show that organisational learning culture is found in a university in Indonesia, and it also shows the importance of applying organisational learning culture in higher education institutions as it facilitates the learning processes. In conclusion, there is a lack of research addressing this relationship in the university context.

Previous research showed a positive relationship between participative decision making, openness and leadership and organisational learning (Flores et al. 2012 ). Since these predictors are considered part of the organisational culture, the author proposes a positive relationship between organisational learning culture and organisational learning. This study will explore the relationship between the organisational learning culture (as a predictor) and the organisational learning process in the university context. Accordingly, the author hypothesises:

H1 : There is a positive relationship between the organisational learning culture and the organisational learning process.

H1a : There is a positive relationship between system connection and dialogue and inquiry and information acquisition.

H1b : There is a positive relationship between system connection and dialogue and inquiry and knowledge dissemination.

Performance

Organisational performance.

The organisation’s performance depends on the achievement and the progress of the strategy identified by the organisation (Davies and Walters 2004 ; Mohammad 2019 ). Performance needs to meet the organisational strategies and the organisational goals because it shows the organisation’s success. Several studies have mentioned Organisational performance as an outcome of organisational learning (Aragón et al. 2014 ; Mohammad 2019 ). This research focuses on university performance. Few studies have focused on assessing the relationship between organisational learning and organisational performance for example (Bontis et al. 2002 ; Crossan and Bapuji 2003 ; Jyothibabu et al. 2010 ; Kontoghiorghes et al. 2005 ; Sun et al. 2008 ). Some previous empirical studies proposed the positive influence of organisational learning on organisational performance (Aragón et al. 2014 ; Mohammad 2019 ). According to previous research, organisational learning helps to improve the performance of an organisation.

Most previous research has focused on the relationship between organisational learning as a capability and performance (e.g., Camps and Luna-Arocas 2012 ; Hurley and Hult 1998 ; Keskin 2006 ; Rhodes et al. 2008 ). Nevertheless, the present study focuses on organisational learning as a process and its impact on university performance. In the context of universities, few empirical studies have shown a positive relationship between organisational learning and university performance (Guţă 2014 ; Pham Thi Bich and Tran Quang 2016 ). Guţă ( 2014 ) did not assess the relationship empirically, while Pham Thi Bich and Tran Quang ( 2016 ) study assessed university performance in only one university. More research is needed to assess the relationship between the organisational learning process and organisational performance (Pham Thi Bich and Tran Quang 2016 ). This paper assesses university performance from teachers’ opinions from several universities. From the previous research, the author hypothesised:

H2 : There is a positive relationship between organisational learning processes and university performance.

The relationship between organisational learning culture and organisational performance has also been discussed in the previous literature, where a positive relationship has been identified between organisational learning culture and organisational performance (Ellinger et al. 2002 ; Sorakraikitikul and Siengthai 2014 ). Organisational learning culture supports promoting and facilitating workers’ learning, hence contributing to organisational development and performance (Rebelo and Duarte Gomes 2011 ). Although there is little empirical evidence concerning the relationship between organisational learning culture and the performance of public organisations, some studies still allow us to infer that organisational learning culture is related to performance (Choi 2020 ). This paper assesses educators’ opinions in public universities, as public organisations are understudied. Hence, we hypothesised the following:

H3 : There is a positive relationship between the organisational learning culture and university performance.

The study will also assess the mediation of the organisational learning process in the relationship between organisational learning culture and university performance. Building on the previous hypotheses H1, H2 and H3, we propose that organisational learning culture solely is not sufficient to improve the university’s performance and that there is a need to involve organisational learning to enhance the university’s performance. Subsequently, we hypothesise the following:

H4 : The organisational learning process mediates the relationship between organisational learning culture and university performance.

Sustainable performance

Nowadays, sustainability has been called for in different business models (Zhang et al. 2019 ). The concept of sustainability helps organisations to improve different processes, which results in higher organisational performance (Zhang et al. 2019 ). Other studies have mentioned sustainability as an output of organisational learning (Kordab et al. 2020 ; Bilan et al. 2020 ; Iqbal and Ahmad 2021 ). The more learning that happens on an organisational level, the more sustainable the organisation is. In a study developed by Bilan et al. ( 2020 ), the authors advised that organisational learning significantly improves the firm’s sustainability. Other studies have focused on sustainable performance (Kordab et al. 2020 ; Iqbal and Ahmad 2021 ). Kordab and his colleagues mentioned the positive relationship between organisational learning and sustainable organisational performance (Kordab et al. 2020 ). The study developed by Iqbal and Ahmad ( 2021 ) states that organisational learning significantly influences sustainable performance. Another study developed in 5 companies in Norway and Italy has explored the internalisation of a sustainable environment through the learning process (Bianchi et al. 2022 ; Massimo and Nora 2022 ). The literature discussing the relationship between organisational learning and sustainability is scarce (Kordab et al. 2020 ). The earlier mentioned studies have assessed the relationship between Malaysian manufacturing organisations (Bilan et al. 2020 ), audit and consulting companies in the Middle East (Kordab et al. 2020 ), and small and medium-sized enterprises in Pakistan (Iqbal and Ahmad 2021 ). On the other hand, this study assesses the relationship between organisational learning and sustainable performance in the university context.

Sustainability in higher education institutions helps in the development of regenerative societies. This help is provided by educators as they influence ideologies and perspectives regarding sustainability in society (Leal Filho et al. 2023 ). In the review study developed by (Serafini et al. 2022 ) for the articles related to higher education institutions and SDGs, only four per cent of the studies considered professors as the target audience. Hence, in this study, the author assesses sustainable performance from educators’ perceptions as it is scarce. Therefore, the hypothesis is developed as below:

H5 : There is a positive association between organisational learning and sustainable performance.

H5a : There is a positive relationship between information acquisition, knowledge dissemination and sustainable environmental performance.

H5b : There is a positive relationship between information acquisition, knowledge dissemination and sustainable social performance.

Finally, Fig. 1 shows the proposed model of this study, which includes the proposed hypotheses.

Proposed research model.

Methodology

Data collection and sample.

This study’s sample mainly focused on university teachers from several European universities. Self-selection sampling is used in this paper; this method helps the researcher better explore the research area and understand the relationships (Saunders et al. 2007 ). The self-selection sampling method relies on the willingness of the participant to participate in the questionnaire. An email invitation with the link to the online questionnaire has been sent to a range of professors. Moreover, university teachers who accepted to participate are the ones who were considered in this study. The researcher has gathered the emails of the university teachers from the university websites.

Data were gathered through an online questionnaire Footnote 1 . The questionnaire is developed from the previous literature. The questionnaire was developed on Qualtrics, and an anonymous link was sent to the respondents. The researcher sent the questionnaire to 10366 university teachers. The university teachers are from different schools and departments, including business, psychology, science, and engineering schools. The researcher received 525 replies, which corresponds to a response rate of 5%. Of the 525 responses received, 304 were incomplete, so we excluded the incomplete questionnaires and kept 221 questionnaires. The questionnaire was sent to 53 public universities in Europe. The countries were Portugal, Spain, Italy, France, Germany and Greece.

The sample is composed of (36.7%) associate professors, (15.4%) assistant professors, (22%) full professors, (6.8%) lecturers and assistants, (3.2%) invited assistant/associate/full professors and (15.8%) respondents who did not declare their job level. Also, the majority of the sample (65.2%) has worked more than seven years in the same university and more than five years in the same team (66.5%). As for the age of the participants, (49.9%) were of age 50 years and above, and the majority of the sample (47.5%) was composed of Males, followed by (37.6%) Females and (14.9%) ‘don’t prefer to say’. The survey was conducted from October 2022 to January 2023. Two reminder emails were sent to the university teachers. Moreover, the design of this study is a correlational design, where all the proposed relationships are studied between organisational learning as a process and its antecedent and outcomes.

The constructs used to assess the indicators in this study are obtained from previous scientific studies, where their reliability and validity were previously tested and verified. Organisational learning culture was assessed using the measure of the Dimensions of Learning Organisations Questionnaire (Watkins and Marsick 1993 , 1997 ), which was adapted and validated to the university’s context in Elbawab ( 2022b ). The adapted measure included two sub-dimensions. The scale consisted of 8 items that measured the two sub-dimensions: dialogue and inquiry and system connection. The participants indicated to what extent they agreed with each of the eight items on a 7-point rating scale (1 = totally disagree, 7= totally agree). An example of the items is: “In my university, whenever academic staff state their view, they also ask what others think.” we checked the scales’ internal consistency to measure these indicators by calculating Cronbach’s alpha. The results indicate strong scale reliability for both system connection (0.88) and dialogue and inquiry (0.87).

The organisational learning process scale was assessed based on the Santos-Vijande et al. ( 2012 ) scale and then adapted to the university’s context in Elbawab ( 2022b ). The scale consists of 8 items that measure two subdimensions: information acquisition and knowledge dissemination. Individuals indicated to what extent they agreed with each of the eight items on a 7-point rating scale (1 = totally disagree, 7 = totally agree). An example of the items is: “We have a meeting schedule among departments and with the dean to integrate the existing information.” We checked the scales’ internal consistency to measure these indicators by calculating Cronbach’s alpha. The results indicate strong scale reliability for information acquisition (0.89) and knowledge dissemination (0.79).

As for the performance, one variable was used to evaluate university performance, and another was used to evaluate sustainable performance. The university performance questionnaire is based on Jyothibabu et al. ( 2010 ), but the scale is adapted to the university context. The measured scale includes seven items. All items were scored on a 7-point Likert scale (1 = totally disagree, 7= totally agree). An example of the items is: “There is continuous improvement in my university”. In this study, the Jyothibabu et al. 2010 scale has been adjusted from a 6-point to a 7-point Likert scale as the 7-point Likert scale reaches the upper limit of reliability (Allen and Seaman, 2007 ; Leung, 2011 ). Also, removing a neutral point introduces “a forced choice in the scale” (Allen and Seaman 2007 ), whereas our focus in this study is to avoid the forced choice. The reliability score of this scale in this study is 0.93; however, Jyothibabu et al. ( 2010 ) Cronbach alpha scored 0.90. In conclusion, the Cronbach alpha score in this study has improved. All factor loadings are significant ( p  < 0.05) and indicate strong factor loadings. As for sustainable performance, it is assessed based on Iqbal and Ahmad ( 2021 ), but only the environmental and social performance were adopted in this study. Since the economic performance measure mainly focuses on sales growth, income stability, profitability and return on investment. At the same time, the activities of public universities are driven by the pursuit of excellence and prestige maximisation, which does not necessarily imply economic efficiency traditionally assumed for profit-maximising business establishments (Kipesha and Msigwa 2013 ). Therefore, sustainable economic performance will not be assessed in this study for these reasons.

These measures are relevant to the university context, which follows the QS world rankings, where the environmental impact and the social impact of each university are addressed. The sustainable performance scale is adapted to the university context. All items were scored on a 7-point Likert scale (1 = totally disagree, 7 = totally agree). An example of the items is: “Your university is concerned about waste management”. The results indicate strong scale reliability for sustainable environmental performance (0.88) and sustainable social performance (0.82). All factor loadings are significant ( p  < 0.05) and indicate strong factor loadings.

Data analysis

The author assessed the descriptive statistics in this study by identifying the means and standard deviations. Also, the author measured the factor loading for all the questionnaire items. Moreover, the author tested the hypotheses with the statistical software IBM SPSS Statistics Suite, version 27. The author assessed the correlations among the relationships and assessed the models using regression analysis using SPSS. Moreover, the author used the PROCESS macro by Andrew Hayes ( 2013 ) in SPSS to test the mediation hypothesis.

The means, standard deviations, and correlations for all the variables in the study are shown in Table 1 . The results shown are a consideration of a sample where more than 65% have worked in the same university for more than seven years, and more than 74% of the sample are assistant, associate, and full professors. The highest means are for performance and sustainability environmental performance. At the same time, the lowest means are for system connection and dialogue and inquiry. Factor loadings were then calculated for all items, which were all higher than 0.64% (shown in Table 2 ).

The findings show the acceptance of H1, there is a strong significant positive relationship between dialogue and inquiry and information acquisition ( r  = 0.70, p  < 0.001), and there is likewise a strong positive significant relationship between dialogue and inquiry and knowledge dissemination ( r  = 0.64, p  < 0.001). There is a significant positive relationship between system connection and information acquisition ( r  = 0.59, p  < 0.001), and a significant positive relationship emerged between system connection and knowledge dissemination ( r  = 0.52, p  < 0.001). Multiple regression was run to predict information acquisition from dialogue, inquiry, and system connection. There is a positive effect between dialogue and inquiry and information acquisition (β = 0.520, p  < 0.01); also, there is a positive effect of system connection and information acquisition (β = 0.197, p  < 0.01). These variables statistically significantly predicted information acquisition, R2 = 0.522. All two variables added statistically significantly to the prediction, p  < 0.05. Therefore, H1a is supported. Multiple regression was run to predict knowledge dissemination from dialogue, inquiry, and system connection. There is a positive effect of dialogue and inquiry on knowledge dissemination (β = 0.544, p  < 0.01), and there is a positive effect between system connection and knowledge dissemination (β = 0.162, p  < 0.01); these variables statistically significantly predicted information acquisition, R2 = 0.429. All two variables added statistically significantly to the prediction, p  < 0.05. Therefore, H1b is supported. These findings show the positive relationship between organisational learning culture and organisational learning.

The findings also support H2. A significant positive relationship between information acquisition and university performance is found ( r  = 0.58, p  < 0.001), and there is a strong positive significant relationship between knowledge dissemination and university performance ( r  = 0.64, p  < 0.001). A multiple regression was run to predict university performance from information acquisition and knowledge dissemination. There is a positive effect between information acquisition and university performance (β = 0.250, p  < 0.01); also there is a positive effect between knowledge dissemination and university performance (β = 0.381, p  < 0.01). These variables statistically significantly predicted information acquisition, R2 = 0.451. All two variables added statistically significantly to the prediction, p  < 0.05. Therefore, H2 is supported. These findings show the positive relationship between the organisational learning process and university performance.

The findings support H3, where there is a strong significant positive relationship between system connection and university performance ( r  = 0.57, p  < 0.001), also there is a strong positive significant relationship between dialogue and inquiry and university performance ( r  = 0.66, p  < 0.001). Multiple regression was run to predict university performance from system connection, dialogue and inquiry. There is a positive effect between system connection and university performance (β = 0.184, p  < 0.01), and there is a positive effect between dialogue and inquiry and university performance (β = 0.440, p  < 0.01). These variables statistically significantly predicted information acquisition, R2 = 0.470. All two variables added statistically significantly to the prediction, p  < 0.05. Therefore, H3 is supported. This concludes the positivity of the relationship between organisational learning culture and university performance.

This study also developed a mediation analysis, H4, using Hayes ( 2013 ). Macros were developed to assess the mediation analysis of the models on SPSS. Macros help estimate the indirect effect with a bootstrap approach (Cole et al. 2008 ).

Organisational learning culture (dialogue and inquiry and system connection) has an indirect effect on university performance mediated by organisational learning processes (information acquisition and knowledge dissemination), which supports H4. Dialogue and inquiry have an indirect impact on university performance mediated by information acquisition (IE = 0.1427). The indirect effect is statistically significant; a bootstrapped 95% confidence interval around the indirect effect did not contain zero CI[0.482, 0.2423]. Also, system connection indirectly affects university performance mediated by information acquisition (IE = 0.1800); the indirect effect is a statistically significant bootstrapped 95% confidence interval around the indirect effect that did not contain zero, CI[0.1104,0.2548]. Dialogue and inquiry have an indirect impact on university performance mediated by knowledge dissemination (IE = 0.2038). The indirect effect is a statistically significant bootstrapped 95% confidence interval around the indirect effect that did not contain zero, CI [0.1306, 0.2865]. Moreover, system connection has an indirect effect on university performance mediated by knowledge dissemination (IE = 0.1970). The indirect effect is a statistically significant bootstrapped 95% confidence interval around the indirect effect that did not contain zero, CI [0.1351, 0.2638].

The findings support H5, where there is a strong significant positive relationship between Information acquisition and sustainable environmental performance ( r  = 0.57, p  < 0.001), Whereas a positive significant relationship has been found between information acquisition and sustainable social performance ( r  = 0.559, p  < 0.001). Moreover, a positive significant relationship is detected between knowledge dissemination and sustainable environmental performance ( r  = 0.526, p  < 0.001), and finally, a positive significant relationship is detected between knowledge dissemination and sustainable social performance ( r  = 0.550, p  < 0.001). A multiple regression was run to predict sustainable environmental performance from information acquisition and knowledge dissemination. There is a positive effect between information acquisition and sustainable environmental performance (β = 0.365, p  < 0.01); also, there is a positive effect between knowledge dissemination and sustainable environmental performance (β = 0.208, p  < 0.01). These variables statistically significantly predicted information acquisition, R2 = 0.362. All two variables added statistically one significantly to the prediction, p  < 0.05. Therefore, H5a is supported. A multiple regression was run to predict sustainable social performance from information acquisition and knowledge dissemination. There is a positive effect between information acquisition and sustainable social performance (β = 0.299, p  < 0.01); also, there is a positive effect between knowledge dissemination and sustainable social performance (β = 0.252, p  < 0.01). These variables statistically significantly predicted information acquisition, R2 = 0.365. All two variables added statistically significantly to the prediction, p  < 0.05. Therefore, H5 b is supported. It is deduced that a positive relationship exists between the organisational learning process and sustainable performance. The results are summarised in Table 3 , and the new proposed model is found in Fig. 2 .

Deduced research model.

Discussion and conclusion

This paper examines the impact of the organisational learning culture on the organisational learning process in the university context as well as the impact of the organisational learning process on a university’s performance and sustainable performance. The literature showed a gap where organisational learning processes are rarely assessed in universities (Abu‐Tineh 2011 ; Elbawab 2022b ). Also, most of the previous studies assess the impact of culture on organisational learning, but few studies have assessed the impact of organisational learning culture on the organisational learning process. Another gap has emerged, where the impact of organisational learning on sustainability is understudied (Alerasoul, 2022 ). Whereas this study also focuses on empirically assessing the relationship between organisational learning and sustainable performance. Learning normally empowers the occurrence of sustainability in organisations and enhances sustainability practices.

The findings of this study contribute to the literature in many ways. The findings support the positive relationship between an organisational learning culture and an organisational learning process, which supports H1. Organisational learning culture is represented by dialogues and inquiry and the system connection. Further, organisational learning processes are represented in this study as the process of information acquisition and knowledge dissemination. Dialogue and inquiry and system connection have a positive impact on information acquisition and knowledge dissemination. So, the findings indicate that the more the organisational learning culture increases in the university, the more the organisational learning processes occur. This relationship between the organisational learning culture and the organisational learning process is assessed empirically in this research, contrary to other studies where previous research always focused on organisational culture rather than organisational learning culture (Cho et al. 2013 ; Liao et al. 2012 ). This research also contributes to organisational learning research as the results indicate that the organisational learning culture is one of the antecedents to the organisational learning process. Accordingly, top management in universities needs to focus on improving the organisational learning culture to have better organisational processes. Also, human resources practitioners need to highlight the importance of maintaining organisational learning culture in organisations as it facilitates the organisational learning process.

The findings also support H2 which posits the positive relationship between the organisational learning process and a university’s performance. Our findings agree with previous research that supported the positive relationship between organisational learning and performance in organisations (Aragón et al. 2014 ; Bontis et al. 2002 ; Jyothibabu et al. 2010 ). We mainly focus on organisational learning processes that enhance a university’s performance. Our findings show that the better the information acquisition process and knowledge dissemination processes are, the better the university’s performance is going to occur in universities. So practically, the higher the efficiency of the acquisition of knowledge process, like acquiring the information from two sources is leading to a better the university’s performance. The two sources of information acquisition are internally from within the university and externally from other universities in the market. In this research, information acquisition is the process of identifying tendencies and problems, which leads to a better performance by the university.

This research has focused on the relationship between organisational learning culture and university performance. The findings in this study agree with previous studies (Ellinger et al. 2002 ; Sorakraikitikul and Siengthai 2014 ) that there is a positive relationship between the organisational learning culture and performance; the present study assessed this relationship empirically in the context of public universities while other studies have focused on various industries. (Choi 2020 ) focused on the assessment of the relationship between organisational learning culture and performance in public organisations generally. The present study focused on public universities as part of the public organisations in any country. The results show support for H3. Moreover, the findings suggest that universities that have a supportive learning culture will lead to better performance. If universities encourage a strong learning culture among their teachers, staff, and students, this will eventually lead to better performance.

Moreover, the organisational learning process mediates the relationship between organisational learning culture and university performance, as illustrated by the findings. These results support H4. Also, these results indicate that the more the top managers and human resources practitioners in universities encourage a learning culture and develop strong organisational learning processes, the higher the university’s performance will be. These findings demonstrate the need for alignment between the university learning culture and the university learning processes as they will promote better university performance. The findings indicate that organisational learning culture indirectly impacts university performance when it is mediated by organisational learning. From a theoretical lens, the organisational learning in this study is stated as a process. Hence, the organisational learning culture is considered an antecedent to the organisational learning process. In this study, the adapted model of culture (system connection and dialogue and inquiry) tests the culture with the process (information acquisition and knowledge dissemination); the results have shown both a direct and an indirect impact on university performance. The direct impact of culture on university performance has been found in the (Wahda, 2017 ) study, where this study agrees with (Wahda 2017 ) study. However, since this study has looked at organisational learning as a process and culture as an antecedent, it provided additional theoretical evidence of the indirect impact of organisational learning culture on performance, mediated by organisational learning as a process. This study also agrees with (Rebelo and Gomes 2017 ) study, where organisational culture emerges as a key concept and an essential condition that would promote and support learning in organisations. Moreover, in the following study, organisational learning culture is considered an antecedent to the organisational learning process. Whereas university performance is considered an outcome of the organisational learning process. Therefore, this study supports the (Rebelo and Duarte Gomes 2011 ) study that considers organisational learning culture as an antecedent to the organisational learning process. At the same time, the theoretical contribution that lies in this study is to address the model and consider the relationship between organisational learning culture and university performance directly and indirectly through the organisational learning process.

Another part of the framework assessed in this study is the impact of organisational learning processes on sustainable performance. Most universities nowadays focus on the importance of sustainability and apply sustainability practices. Also, the universities capitalise on the SDGs that are developed by the European Union. Whereas universities call for relating future research with the SDGs, as it helps in the sustainable development of society (Serafini et al. 2022 ). Moreover, universities focus on enhancing their sustainable environment. Still, this study addressed a significant gap, where few research studies have empirically addressed the impact of organisational learning on sustainable performance. And this research highlights the importance of applying this relationship and helps imply sustainable performance as an indicator for universities to be used in the future.

Sustainable performance in this study is considered another outcome of organisational learning. The findings of this study show support for H5. This study confirms the previous study findings (Iqbal and Ahmad 2021 ) that there is a positive impact between organisational learning and sustainable performance. However (Iqbal and Ahmad 2021 ) study did not assess the organisational learning process in universities, so this study develops it. The findings of this paper indicate the positive impact of information acquisition and knowledge dissemination on sustainable environmental performance and sustainable social performance. Therefore, when the organisational learning in universities is deepened and increased, the sustainable performance of the universities is better.

Finally, this study has created a suitable model to assess organisational learning processes, antecedents, and outcomes in universities. It has contributed to the theory of organisational learning; it shows a newly adapted model of organisational learning culture as well as organisational learning as a process and its influence on a university’s performance and the sustainable performance of the organisation. This adapted model may be considered novel because it indicates the antecedents, processes, and outcomes of organisational learning. Also, this model relates between NRBV and organisational learning theories, which is considered a theoretical contribution. Moreover, this model is tested in European universities and is considered an empirical contribution.

Implications and future research

Theoretical implications.

For the theoretical implications of describing organisational learning in universities, most previous research focused on assessing learning organisations; for example, the review developed by (Örtenblad and Koris 2014 ) showed the studies focused on assessing learning organisations. Furthermore, few studies focused on the learning processes and identified which organisational learning processes are more relevant to public universities and the education sector.

We have validated the organisational learning predictor as an organisational learning culture. Also, we have validated the organisational learning outcomes: university performance and sustainable performance.

The relevant learning processes are knowledge dissemination and information acquisition, where these processes are mainly representing the organisational learning processes in universities. This study recommends using the organisational learning developed model as it is relevant to universities.

Since researchers proposed that organisational learning culture is an important facilitator of the organisational learning process (Marsick and Watkins 2003 ). This study focused on assessing organisational learning culture’s impact on the organisational learning process. Previous research mainly focused on assessing the organisational culture’s impact on organisational learning (e.g., Oh and Han 2020 ; Rebelo and Duarte Gomes 2011 ). Few researchers focused on the impact of organisational learning culture on organisational learning. Previous research showed organisational culture as decision-making processes, openness, learning orientation, and leadership (Flores et al. 2012 ). In this study, organisational learning culture was described as the collective learning culture that enhances the organisational learning activities (Sorakraikitikul and Siengthai 2014 ). Subsequently, it is evident that this gap was addressed and studied in this study. The findings show a strong relationship between organisational learning culture and organisational learning processes in education.

Moreover, the mediation of organisational learning processes between organisational learning culture and university performance is another theoretical contribution to organisational studies. As the previous research mainly focused on the impact of organisational learning culture on organisational performance (Sorakraikitikul and Siengthai 2014 ), few studies focused on mediation analysis. The findings of this study show that organisational learning culture indirectly impacts university performance when it is mediated by both organisational learning processes (information acquisition and knowledge dissemination). These findings show the importance of having an effective learning culture and efficient organisational learning processes on the organisational level as they help improve university performance.

Previous studies focused on assessing the relationship between organisational learning and organisational performance (Bontis et al. 2002 ; Jyothibabu et al. 2010 ). Scarce studies focused on university performance, while in the meantime, university performance reveals the success of the university and the achievement of its goals. Finally, this study has contributed to organisational studies by exploring the relationship between organisational learning and sustainable performance and curating a model specifically for higher education institutions.

Practical implications

This research serves universities; the implications of this research could be adapted to various faculties. The result of this investigation recommends that when universities work on organisational learning culture, it is followed by enhancing the organisational learning process. Subsequently, the organisational performance and sustainable performance will be improved. This section will provide implications and countermeasures for different stakeholders related to the universities.

To develop the organisational learning culture, universities should work on their dialogue and inquiry process and their system connection process. A learning culture that promotes more dialogue and inquiry among the university members is the target. Hence, the organisational learning culture should be encouraged at all university levels, including among deans, heads of departments, directors, and teachers. The author of the study suggests that deans hold organisational meetings for the faculty members (for example, semester meetings and monthly meetings) mainly to discuss the faculty’s point of view, to share feedback and to empower the experimentation and questioning of the process, to reach for shared view and perspective reasonably. For the teacher, the author recommends attending the meetings with the capacity to inquire about the process, share their feedback and opinions and listen to other views. At the same time, the author recommends enhancing the organisational learning culture by focusing on the system connection.

The author recommends having an ongoing connection with the market to know their needs. Subsequently, the department heads and the director of the programs design new curricula and update the existing ones to align the curriculum with the market needs. We recommend that the teachers support the system connection by advising and recommending the new market demands and technologies in their classes and for the program directors and department heads. Subsequently, both these actions will help increase the organisational learning culture in universities.

After providing the necessary organisational learning culture, universities should develop their organisational learning process in order to increase their performance. To develop the organisational learning process, universities are recommended to capitalise on information acquisition and processes to disseminate knowledge.

For the information acquisition process, acquiring information from two sources improves the university’s performance. The two sources of information acquisition are internally from within the university and externally from other universities in the market. The author recommends that the deans, as well as the heads of departments, focus on acquiring the information internally, whether the students’ satisfaction, the feedback about the curriculum, and even the successful teaching methodologies. The deans need to focus on the university’s previous experiences and align the learning processes with the business environment. As for the external information, deans and rectors who have the bigger image need to acquire the information from competitors (e.g. international universities) and the marketplace to develop their analyses regarding each academic year and the long-term plans. The rectors will help empower the organisational learning processes to flow throughout the university. Practically, the top management in the university needs to look for the best practices internally and externally and solve problems by identifying the key tendencies, whereas in this process, the rectors, deans, program directors, heads of departments and teachers need to share and collaborate. Meanwhile, the information acquisition process is enhanced by collecting not only the information but also the best practices and pedagogies from the competitors. Also, the employers’, policymakers’, and governors’ perspectives should be acquired.

Another practice for enhancing the information acquisition process is comparing the university’s performance to other universities. Acquiring information sources could be formally through the formal reports published on a yearly basis (ex: public university performance reports, universal rankings and amount of collaboration and funds provided to this university), whereas informal sources of acquiring information like assessing the performance of the university on social media. Hence, different stakeholders are involved in the information acquisition process.

As for the second process, knowledge dissemination, the author recommends that universities focus on both the formal and informal interaction between employees to enhance the dissemination of knowledge. For the formal interactions, the author advises rectors and deans to focus on meetings and training that will enhance the continuous learning process and efficiently use the university database. Moreover, deans should enhance the formal networks among their universities and the rest of the universities, for example, through exchanging professors and publishing research papers. These practices will help disseminate the information rapidly and accurately. As for the informal interaction and communication between employees, the author advises the teachers to share knowledge, best practices, new teaching methods, and new tendencies in research and the market with each other. The author also advises university heads of departments to encourage informal interaction between teachers to support the university’s organisational learning. Since the government governors are involved in the process, the author recommends that university heads contact government governors. Government governors can help by disseminating the goals and future plans of the government, as well as the best practices from different universities so that the universities can adapt their learning processes.

Another example of an organisational learning process may be found during the departmental and pedagogical meetings, during which the future of the courses, the programmes, and the schools are discussed, this leads to better dissemination of knowledge and eventually a better performance by the university. Therefore, top managers in universities need to focus on the implementation of the organisational learning processes in their universities as it helps in having better performance and eventually adapt to change and university success (Meshari et al. 2021 ).

Another important point is that universities need to acquire sustainable performance as it is also one of the indicators of the university rankings that has been recently added to the university’s ranking (like the QS world ranking). Where the SDGs in the QS world ranking, mainly focus on the environmental impact and the social impact of each university by indicating the SDGs rating of each university regarding social and environmental impact.

Finally, top managers in universities need to adapt the universities to the change that is occurring internationally and promote having a better sustainable performance to also reflect in their ranking. These will eventually reflect in the university’s success.

Since the findings show that there is a positive impact of organisational learning on sustainable performance, then it is recommended to deliver training for top managers and decision makers in civil society organisations and government governors to enhance sustainable development outreach. Moreover, in order to focus on the importance of sustainable performance in the meetings and the conferences that are developed in the organisations.

In conclusion, all these recommendations will likely help universities achieve better performance. Top managers like deans, rectors and school heads in universities need to focus on the learning flow within the university. They need to focus on the organisational learning process at the organisational level. The author suggests for the future of this research stream to assess this model on a broader scope, as more universities from outside Europe could be evaluated. As this model showed its validity in various public universities. Also, it is recommended to assess this model in private universities, as the private universities have different regulations, organisational learning culture and performance goals.

Data availability

The dataset generated during and/or analysed during the current study is available from the corresponding author upon reasonable request.

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• PMCID: PMC11092706
• DOI: 10.1101/2024.05.01.24306704

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Perhaps one of the reasons BPM research concentrates on analytical modeling of business processes is that BPMN is standardized fully in this regard and modeling tools support the notation very well. In this book, we focus instead on empirical research in executable process models. This requires a complete and precise specification of process models, which graduate from “PowerPoint slide” into an executable artifact running inside a workflow engine in the Cloud. In this chapter, we introduce fundamental background concepts defining executable business processes, discussing empirical research methods suitable for business process management, and presenting different architectural options for process execution and close with a brief history leading toward executable BPMN.

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Lübke, D., Pautasso, C. (2019). Empirical Research in Executable Process Models. In: Lübke, D., Pautasso, C. (eds) Empirical Studies on the Development of Executable Business Processes. Springer, Cham. https://doi.org/10.1007/978-3-030-17666-2_1

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• HealWELL AI -stock
• News for HealWELL AI

HEALWELL AI Participates in Leading-Edge Research on Chronic Kidney Disease Highlighting the Potential of its AI Powered Clinical Co-Pilot

• HEAWELL AI’s Clinical Co-Pilot technology participated in recently published evidence showing the integration of a highly accurate machine model for Chronic Kidney Disease (“CKD”) progression when paired with Electronic Health Record (“EHR”) linked clinical decision support, improves guideline-recommended testing in patients with CKD.
• HEALWELL’s subsidiary Khure Health (“Khure”) co-authored an abstract that was published this month in the American Journal of Kidney Diseases, providing evidence of Khure’s Clinical Co-Pilot’s utility to support improved CKD patient care by US Nephrologists.
• CKD is a major health problem affecting approximately one seventh of the population in North America. In Canada alone, the economic burden of chronic and end-stage kidney disease costs the national healthcare system more than $40 billion per year.
• WELL Health Technologies Corp. (TSX:WELL) recently launched WELL AI Decision Support (“WAIDS”), its second-generation AI powered physician Co-Pilot powered exclusively by HEAWELL and available to assist thousands of providers within the WELL ecosystem.

TORONTO, May 16, 2024 (GLOBE NEWSWIRE) -- HEALWELL AI Inc. (“ HEALWELL ” or the “ Company ”) (TSX: AIDX) (OTCQX: HWAIF), a healthcare technology company focused on AI and data science for preventative care, is pleased to announce that it has participated in recently published leading-edge research on Chronic Kidney Disease (“CKD”) in the esteemed American Journal of Kidney Diseases (“AJKD”). HEALWELL’s AI-powered clinical decision support Co-Pilot technology paired with a highly accurate machine model for CKD progression supports US-based Nephrologists to improve guideline recommended testing in CKD patients. This marks a significant validation of the capability of such advanced technology in chronic diseases; an area of particular importance due to the burden of chronic diseases including CKD on the North American population.  HEALWELL’s recently launched second-generation AI Clinical Co-Pilot incorporates similar advanced technology to support physicians with improved patient care.

The American Journal of Kidney Diseases recently published an abstract entitled “Baseline Characteristics and Early Results From The Gemini-Rapa Project: Improving The Quality Of CKD Care With Risk Prediction And Personalized Recommendations” ( 1) , emphasizing how the integration of a highly accurate machine model for CKD progression when paired with Electronic Health Records (“EHR”) linked clinical decision support improves guideline-recommended testing in patients with CKD. This highlights how HEALWELL’s technology can support US specialists in one of the most important chronic disease domains; chronic kidney disease. This abstract (1) , co-authored with BAYER LLC US, Renal Associates PA, and Klinrisk Inc. was also presented this week at the National Kidney Foundation meeting in Laguna Beach, California.

Dr. Alexander Dobranowski, CEO of HEALWELL, added, “Chronic diseases are the leading cause of death and disability in Canada and we couldn’t be more excited to publish in the respected American Journal of Kidney Diseases alongside such reputable lead authors, validating the utility of our advanced technology and how it can leverage other key technology in playing a key role in supporting improved chronic disease patient care.”

On May 2, 2024, HEALWELL announced its partner and shareholder, WELL Health Technologies Corp. (TSX:WELL) (“WELL”), launched the second-generation of WELL AI Decision Support (“WAIDS”), its AI-powered physician Co-Pilot powered exclusively by HEAWELL. This technology is now commercially available to the WELL network which consists of thousands of healthcare providers within the WELL ecosystem. For more information on WELL AI Decision support, please visit: https://decisionsupport.wellhealth.ai/

CKD is a major health problem affecting approximately one seventh of the population in North America. In Canada alone, the economic burden of chronic and end-stage kidney disease costs the national healthcare system more than $40 billion (2) per year. According to a Statistics Canada report in 2023 on the Health of Canadians ( 3 ) , 45.1% of Canadians lived with at least one major chronic disease in 2021 and Canadians with Chronic Kidney Disease comprised 11-13% ( 4 )  of the entire population.  

• Baseline Characteristics and Early Results from the GEMINI-RAPA Project: Improving the Quality of CKD Care with Risk Prediction and Personalized Recommendations , AJKD, VOLUME 83, ISSUE 4, SUPPLEMENT 2, S87, APRIL 2024,   https://www.ajkd.org/article/S0272-6386(24)00335-4/fulltext
• Source: Statistics Canada, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926383/#:~:text=The%20economic%20burden%20of%20chronic,billion%20per%20year%20in%20Canada.&text=Manns%20et%20al1%20estimated,payments%20for%20patients%20with%20CKD.
• Source: Statistics Canada, https://www.statcan.gc.ca/en/about/smr09/smr09_142
• Source: National Library of Medicine, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926383/#:~:text=Chronic%20kidney%20disease%20(CKD)%20is,13%25%20of%20the%20population%20globally.

Dr. Alexander Dobranowski Chief Executive Officer HEALWELL AI Inc.

About HEALWELL AI

HEALWELL is a healthcare technology company focused on AI and data science for preventative care. Its mission is to improve healthcare and save lives through early identification and detection of disease. Using its own proprietary technology, the Company is developing and commercializing advanced clinical decision support systems that can help healthcare providers detect rare and chronic diseases, improve efficiency of their practice and ultimately help improve patient health outcomes. HEALWELL is executing a strategy centered around developing and acquiring technology and clinical sciences capabilities that complement the Company's road map. HEALWELL is publicly traded on the Toronto Stock Exchange (the “TSX”) under the symbol “AIDX” and on the OTC Exchange under the symbol “HWAIF”. To learn more about HEALWELL, please visit https://healwell.ai/

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