Abstract

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Introduction

The building and infrastructure industries are gradually transitioning towards building information modelling (BIM) as an object-based digital representation of an asset’s physical and functional characteristics [1], [2] and digital engineering (DE) practices to enable more productive methods of planning, designing, constructing, operating, and maintaining assets, using digital processes [3]. As detailed later in the paper, “digital” encompasses approaches, tools and technologies such as BIM and DE together with data science, the Internet of Things (IoT), and AI solutions integrated to digitise assets throughout the lifecycle. Asset owners recognise the importance of digital transformation (DT) in delivering and managing assets [4], as it offers many benefits throughout the asset lifecycle and can enhance efficiency, value for money, productivity, innovation, and safety.

1.1. DT of large infrastructure asset ownersGovernments and government-owned corporations (GOCs) are typically owners and operators of large assets, such as buildings, infrastructure, and other asset holdings across the spectrum of services. Governments and industries must transition from ‘digital by exception’ towards ‘digital by default’ [5], [6]. The infrastructure sector should adopt an evidence-based approach to infrastructure planning, delivery, and operations, including BIM, DE, embedded sensors, and digital asset management [7]. DT offers significant potential value and benefits to government agencies that manage assets across the whole-of-asset lifecycle. DT can assist in strategic investing and partnering with local governments and industry to deliver more benefits, including [8]:

Conclusion

The dynamic evolution of the building and infrastructure sectors towards digitalised practices underscores the growing importance of asset owners engaging in comprehensive DT. This research project has developed a multifaceted framework for assessing DT maturity tailored to the needs of large GOCs.

The existing assessment tools and methods have been reviewed for suitability in assessing the DT in whole-of-life asset owner organisations. The reviewed tools offer varying assessment scopes, including a capability assessment, maturity assessment, and benchmarking. The need and potential benefit of a new maturity assessment tool that would balance assessment accuracy and completion efficiency were established based on the shortcomings and limitations of existing DT maturity tools and methods. The key contributions of the new DT maturity assessment framework and tool developed by the research team include (i) a holistic view on the assessment of how well organisations use data and digital solutions, focusing on BIM capabilities, the value of information, data integration aspects, components of the smart city concept and critical information management competencies; (ii) an evidence-based scoring approach including assigning weight to various assessment indicators based on their importance to ensure the accuracy of the assessment; (iii) a multiperspective assessment allowing for more accurate organisation-wide analysis; (iv) short-term and long-term recommendations based on the current and target levels gap to facilitate performance improvement while realising the benefits and value of getting to each maturity level.

Abstract
This paper examines the relation between bank profit performance and business models, using a machine learning–based approach. The analysis contributes to the literature on this relation by considering the bank portfolio’s ability to yield profits as the identification criterion of strategic profiles and by including all the components of the business model simultaneously in the identification process. Our research strategy is applied to the European Union banking system from 1997 to 2021. The paper’s primary finding indicates that specialization seems to be a strategy that results in banks adopting business profiles with better profit performance, particularly if the banks specialize in the standard retail-oriented model.

Introduction

      What characteristics of bank business models enhance profitability? The search for answers has been among supervisory authorities’ priorities since the end of the Great Recession and is likely to maintain its relevance in the aftermath of the COVID-19 pandemic. The reasons for such interest lie in the fact that the relation between business models and profitability can reveal the viability of banks’ strategic plans and, hence, the viability of banks themselves. In this regard, the Anon (2016) links poorly adapted business models to low bank profitability in advanced economies, identifying the former as an eroding agent of bank resilience over time. However, concern about the profitability of banks’ business profiles has been especially strong in the European Union (EU), as reflected by the fact that the European Central Bank launched a thematic review in 2016 to assess the relation between profitability and business models (European Central Bank, 2018).

      Banking research has examined this relation in two broad and different ways, using regression analysis to explore the effects of the characteristics of the bank business profile on profitability or grouping banks into business models by means of clustering techniques.

Conclusion

     The ability of business models to generate profits is an ongoing concern for banking authorities, especially in the EU. Closely related to this concern, we propose a new methodological strategy to identify business models and to assess their relation to profit performance. Previous works on business models have assumed the business models as a given or have identified them using similarities in balance sheet components. In either of these cases, profit performance is a byproduct of the business model. By contrast, in our identification strategy, profitability is at the core of the definitions of banks’ strategic choices. In particular, business models are obtained using the contributions of the bank portfolio components to profitability as the instruments of clustering analysis.

      The empirical exercise focuses on the EU banking system in 1997–2021. According to the classification of business profiles based on their overall contributions to profit performance, the best profile is a standard retail-oriented model, the second-best one is a standard wholesale model, and the worst profile, which has only equity as a characteristic component, does not match any standard model. Therefore, specialization seems to be a strategy that results in banks adopting business profiles that perform better in terms of their contributions to profitability, particularly if the banks specialize in the standard retail-oriented model.

Abstract

     Green innovation is an essential and burning topic for environmental and organizational performance. Therefore, this research aims to examine the effect of green innovation on environmental performance, which leads to organizational performance. Another objective is to measure the impact of two dimensions of green innovation, such as green process & green product measures, on green innovation. The second prime aim of this research is to evaluate the moderation of management commitment & human resource practices in an association between green innovation and organizational & environmental performance. A total of 320 employees provided their perspectives on a self-administrated questionnaire from the textile industry of Pakistan. We have employed SEM-based multivariate modeling to examine the data. This research has measured the reflective indicators measurement model through confirmatory factor analysis, an obvious choice of structural equation modeling to examine observed and unobserved variables and indicators using PLS-SEM (partial least square-structural equation modeling). The research findings reveal a positive & significant effect of product & process innovation on green innovation. Further, green innovation significantly impacts environmental and organizational performance. A two-way interaction (moderation) of human resource practices & green innovation does not have a cogent moderating effect on organizational & environmental performance. However, management commitment has a significant moderation between green innovation & organizational performance. A three-way interaction (moderated moderation) model finds a substantial effect on organizational attainment but an insignificant impact on environmental performance. The research outcomes significantly contribute and suggest that practitioners and policymakers must institutionalize green innovation practices in their organizations to enhance their organizational and environmental performance. HR practitioners play a vibrant role in creating green norms and organizational culture. The study findings also suggest that management commitment to green innovation advocates organization-level transformations toward adopting green practices.

Introduction

     With the advent of technology and scientific advancements, the world has provided immense exposure to artificial resources, making life leisurely. However, artificial resource opulence has come at the cost of sustainability and the environment. The world has thus been facing issues with balance acquisition between sustainable usage of resources and economic development [1,2]. Managing both have been a great concern for the economies as one comes at an expense and tradeoff. Researchers have observed that economic growth and sustainability cannot go together [3,4]. Green growth and general innovation are well recognized for their interrelationship [5]. It is essential to observe that national economic development is established through soaring industrial activities [6]. In this essence, Segerstrom [7] validated through his proposed model that firm-level innovations immensely support economic growth. Green innovation is beneficial to prevent the environment from pollution, help in recycling waste, and could also save non-biodegradable energy [8,9]. Moreover, green technologies could achieve sustainable development [10,11]. In order to implement and achieve the target of green innovativeness, firms need to adopt teamwork within their organizational ecosystems (Muisyo et al., 2022). The said process can be run by key parameters such as the organizations’ access to large-scale data, the commitment of the management, and proper usage of human reserves [12,13], among other considerations. The outcomes demonstrated that firms are enticed to accept green innovation practices when internally and externally motivated organizations. Internal motives combine management’s commitment and supplier’s partnership, whereas external motives are regulations and demand pressures from customers. Thus, there is a direct relationship between the two [14]; (Parmar et al., 2022). Organizations can enhance their performance through green innovation as green practices guide them to devise strategies that can help them use fewer resources, reduce the chances of harm, and create a carbon footprint in the organization [15]. The green revolution is a unique strategy that permits organizations to create new techniques and systems that help organizations enhance their performance [16,17].

Conclusion 

     This study evaluated the connotation between green product & process innovations and green innovation. Moreover, the study examined the impact of green innovation in affiliation with organizational & environmental attainment. The study’s findings explored all the hypotheses devised to meet study objectives. Moderation effects of management commitment towards greening and green human resource practices were also examined between the primary relationship. Primary responses were obtained from the mid-career and upper management of the textile sector of Pakistan. This research revealed that product & process innovations have a cogent and affirmative effect on green innovation. The findings further demonstrated that green innovation exerted an affirmative and cogent effect on organizational & environmental performance. Thus, the senior management should seriously consider green innovation projects to increase the firm’s environmental performance and the long run, sustainable competitive growth. While evaluating the moderating effect of HR practices on environmental attainment, the findings are not supported.

Abstract

     This paper develops an ordonomic approach to the governance of sustainable business model innovation (SBMI). We clarify the distinctive roles of optimization and governance for the management of sustainable value networks and develop a sustainability cube as a new management tool for the governance of SBMI. Our cube helps management to identify and overcome social dilemmas within value networks, i.e. to form and reform relevant business relationships, thus creating and tapping second-order win-win-win potentials. Furthermore, our cube encourages management to interpret negative externality problems as “missing markets”, i.e. as an entrepreneurial challenge and as a business opportunity to serve as yet unmet needs. Finally, our cube offers an avenue to develop and strengthen the specific management competencies that foster a successful governance of SBMI.

Introduction

     After having promoted the sustainability concept of a “triple bottom line” (TBL) for 25 years, Elkington (2018) is rather disillusioned and (self-)critical of what, in effect, has become an accounting concept. Looking back, he clarifies his initial goal—and identifies what went wrong (para. 8, emphasis in original): “[T]he original idea was wider still, encouraging businesses to track and manage economic (not just financial), social, and environmental value added—or destroyed. … [T]he TBL wasn’t designed to be just an accounting tool. It was supposed to provoke deeper thinking about capitalism and its future, but many early adopters understood the concept as a balancing act, adopting a trade-off mentality.” He further elaborates (para. 13, emphasis in original): “TBL’s stated goal from the outset was system change—pushing toward the transformation of capitalism. … It was originally intended as a genetic code, a triple helix of change for tomorrow’s capitalism, with a focus … on breakthrough change, disruption, asymmetric growth (with unsustainable sectors actively sidelined), and the scaling of next-generation market solutions.” Looking ahead, he states (para. 16 f.): “[W]e need a new wave of TBL innovation and deployment. … Hence the need for a ‘recall.’ I hope that in another 25 years we can look back and point to this as the moment [we] started working toward a triple helix for value creation, a genetic code for tomorrow’s capitalism, spurring the regeneration of our economies, societies, and biosphere.”

Conclusion and outlook

     This paper develops an ordonomic approach to the governance of SBMI. We clarify the distinctive roles of optimization and governance for the management of sustainable value networks and develop a sustainability cube as a new management tool for the governance of SBMI. Our cube helps management to identify and overcome social dilemmas within value networks, i.e. to form and reform relevant business relationships, thus creating and tapping second-order win-win-win potentials. Furthermore, our cube encourages management to interpret negative externality problems as “missing markets”, i.e. as an entrepreneurial challenge and as a business opportunity to serve as yet unmet needs. Finally, our cube helps management to develop and strengthen the specific competencies that foster a successful governance of SBMI.

     As an outlook, we would like to emphasize several limitations of our contribution in this paper, which may stimulate further research.

     First, we deliberately skipped an important aspect. Since we wanted to concentrate on sustainability opportunities beyond the organizational boundaries of the firm, we edited out the ordonomic strategy of individual self-commitments. We therefore concentrate on perceiving the organization and its interior structures as a monolithic block. On the one hand, this assumption needs to be highlighted as a serious limitation of our tool, but on the other hand it provides a vital starting point for complementary further research on intra-organizational challenges such as culture, mind-set, etc. (see e.g., Evans et al., 2017; Geissdoerfer et al., 2018) and tensions’ management in SBMI (see e.g., Schultz, 2022a). Hence, managers using the cube must be constantly aware of this blind spot. Further research could explore the possible interplay between the three commitment strategies (external relations management) embodied in the cube and the one we left out (internal relations management). For the latter, we would like to hint to an initial ordonomic contribution along those lines cf. Will and Pies (2018).

Abstract

Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

Introduction

Nowadays, generalized adoption of the new manufacturing paradigms involves the assimilation of key technologies such as intelligent data analysis and machine learning (ML), among others, aiming at the digital transformation of enterprises [ 46 , 56 ]. This digital transformation is crucial to keep up with the competition, especially in the case of small and medium-sized manufacturing enterprises (SMEs). One contemporary target is to create a highly reconfigurable, decentralized, dynamic, self-organizing, and real-time (or near real-time) decision-making infrastructure enabling to analyze customer expectations and reach their targets [ 20 ]. By applying these transformations, SMEs should be capable of monitoring and improving their key performance indicators (KPIs) [ 67 ]. However, in practice, SMEs face several difficulties in applying these technologies, mostly related to the transition and maintenance costs, innovation complexity, and personnel training [ 50 ]. Additionally, due to limited human and computational resources, time constraints and complexity of the optimization processes, SMEs usually focus their efforts on a single productivity objective, despite being more desirable to consider and optimize multiple objectives, which leads not only to more efficient but also a more sustainable and environmentally friendly production. Due to these obstacles, a large number of SMEs don’t count yet with the necessary tools to continue with their digital transformation. In this context, the development of tools for generating useful information and smart recommendations of production systems in SMEs is almost mandatory in a high-competitive market and has a large number of potential adopters [ 12 , 42 ].

Conclusions

This work presents an automated machine learning methodology for optimizing manufacturing processes in SMEs by combining the standard tasks of AutoML tools, such as data preprocessing, feature selection, model training, and hyperparameter optimization, with preference-based multi-objective optimization. For this purpose, the basic AutoML workflow is used to generate models for each of the KPIs of the production process and, then, a new automated optimization step is introduced for using the generated models as objective functions, resulting in optimal parametrizations of the production process. By simplifying the way of interacting with this methodology, it is possible that manufacturing SMEs with low availability of highly-skilled personnel or limited computing power can benefit from advanced technologies making easier the digitalization and application of Industry 4.0 paradigm.

The methodology was implemented and validated in a production process where, firstly, the most relevant features for modeling each key performance indicator were automatically selected based on the Pearson’s correlation coefficient, allowing to reduce the dimensionality of the data. Then, models of key performance indicators were generated and their architecture/hyperparameters optimized. Generated models were compared to models obtained through other AutoML frameworks offering similar results, with values of MSE = 2.585 and R2 = 0.999, and MSE = 6 × 10−5 and R2 = 0.735, respectively. Finally, the models were used as objective functions in the R-NSGA-II algorithm for finding optimal parametrizations of the production process, yielding an improvement in both KPI, reducing scrap by 2.15 % and increasing throughput by 3.19 %, with regard to the baseline of conventional parametrization considering only a single productivity target. These improvements contribute to a higher production rate while, at the same time, the number of defective components is reduced, which underscore the potential of the proposed methodology to significantly boost overall efficiency and profitability for SMEs by optimizing their production processes more holistically.

Abstract

This study aims to analyze the role of green transformational leadership and human resource management in organizational performance with the mediating effect of green innovation. The study also proposes the moderating role of commitment to management change. The study used a quantitative research method with a random sampling technique and surveyed 235 managers from the Cement industry across Pakistan. The statistical techniques used to assess the study’s hypotheses were partial least squares structural equation modeling (PLS-SEM) and SPSS. The findings show that green transformational leadership and HRM positively impact organizational performance, with green innovation partially mediating effect. Furthermore, affective commitment to management change strongly moderates the relationship between green innovation and organizational performance. In addition, the proposed relationship is based on the resource-based view (RBV) theory. Therefore, Managers and executives should adopt green principles and practices and build an environmentally friendly culture that encourages employees to engage in green behaviors and activities to improve organizational performance. To successfully implement green practices in a developing country like Pakistan, the Cement sector must understand how the cement industry improves its performance and whether employees demonstrate green innovation.

Introduction

In recent decades, scholars have recognized the significance of Green HRM and green leadership in shaping the operational practices in modern firms [1]. In this regard, and for the efficient and effective management of operations, technology advancements, and rising stakeholder pressure have motivated firms to discover and implement cost-effective and long-term methods to improve organizational performance [2,3]. Enhancing performance has emerged as a primary problem for businesses, requiring the effective utilization of organizational resources and adopting environmentally sustainable practices in Green HRM and eco-friendly activities [4]. The significant rise in globalization and social responsibilities demands justifications for adopting optimal Green HRM practices and integrating data analytics to enhance decision-making [5,6]. To ensure the sustainability and advancement of green business activities, dominant policies encompassing Green HRM, innovation, leadership, environmental quality, and social well-being are essential. Businesses can mitigate their adverse environmental and societal impacts by implementing GHRM, green leadership, and innovative practices [7]. Green HRM comprises three crucial components: motivating employees to enhance their engagement, providing them with green opportunities, and assisting in developing their green skills, which are essential abilities [8]. The underlined components of Green HRM address staff recruitment, selection, development, and training with an environmentally conscious focus; the potential for adopting green practices encompasses employee involvement and green leadership [9].

Discussion

Our empirical work has examined the interactions among variables such as green transformational leadership, HRM, innovation, affective commitment to management change, and organizational performance of the cement industry in Pakistan using theoretical insights from RBV theory. Our study has thoroughly explained all three hypotheses and their sub-hypotheses, showing that the performance of an organization can be improved through green transformational leadership and HRM. This improvement is achieved through the mediating role of green innovation and the moderating role of affective commitment to management change. There is a strong and positive association between all three hypotheses.

Our study found that green transformational leadership influences firm performance by establishing a clear environmental vision, motivating individuals to adopt green practices, increasing employee commitment to achieving environmental goals, and encouraging staff to create and disseminate new green practices; these results are similar to past findings [41]. The results demonstrate that transformational leaders who foster their employees’ green practices to motivate them have excellent potential to execute green prospects effectively.

Abstract

The project selection criteria are required for an effective project lifecycle, and the selected projects should correspond with organizational aims and objectives; yet, the organization lacks severely in its ability to act decisively on the problem. Hence, this study aims to highlight the best project selection criteria that are related to organizational success. In this manner, a quantitative survey approach was used, with closed-ended questions distributed to employees in Pakistan’s construction industry. The relationship between project selection criteria and organizational performance was evaluated using structural equation modelling in SmartPLS through measurement and structural models. The findings reveal that a significant positive correlation was found for all the project selection criteria, except for the financial criteria. Furthermore, the results demonstrate that project selection criteria are critical for organizational success; hence, the findings will assist organizations in making better project selection decisions.

Introduction

Organizations choose projects based on the many potential prospects that is why it is regarded as one of the most difficult decisions to make [1] . There are various aspects associated with project selection, such as cost, financial, and time-based criteria [2] and any mistreatment may result in an outbreak of an obstacle [3] , [4] . An organization’s project selection problems develop when there are inadequate resources to take advantage of; hence, project selection criteria are devised to select the cost-effective projects [5] , [6] . A significant part of project management-related research revolves around organizational performance [7] which is critical for the success and survival of today’s numerous commercial and economic operations due to market rivalry for cash, consumers and inputs [8] . Organizational performance is central to today’s industrial activity [7] , [9] and is considered a collection of all the businesses accomplishments which are then committed towards a structural goal for organizations in a given period. The notion of organizational performance is linked with the persistence and achievements of a firm [8] , [10] where several constructs have been linked and studied afterwards [11] . Organizational performance measures comprise several financial measures including budgeting, cash flow cash flow, cost-cutting, and profitability [12] .

The criteria used to select projects can vary depending on the organization’s goals, industry, and available resources. The relationship between project selection criteria and organizational performance is complex and multidimensional [13] , [14] . The influence of project selection on organizational performance has been studied in various fields, including information technology, engineering, and construction. A few studies suggest that selecting projects based on strategic alignment and financial feasibility can improve project outcomes and overall organizational performance [15] , while other studies have found that selecting projects based on risk and organizational capability can lead to better project outcomes and performance [16] . Hsu [17] investigated the relationship between project selection and organizational performance in the construction industry and found that project selection criteria, such as financial viability, strategic fit, and technical feasibility, had a significant impact on project success. Similarly, Lind and Culler [18] investigated that the effect of project selection on the performance of information technology projects and found that effective project selection was positively associated with project success and organizational performance. Beldek, et al. [19] studied the financial, social, technical, and risk criteria in the context of Turkish industries, however, the study was limited to the number of selection factors. Hence, adding upon on their outcomes, the current research has added two more criteria namely environmental and management support criteria in the context of Pakistani industries as supported by Bhatti, et al. [11] .

Conclusion

The relationship between project selection criteria and organizational performance is complex and multidimensional. Few studies have investigated this relationship in service industries and identified institutional, financial, risk and technical criteria as key criteria for project selection, however, still, there is a gap in the body of knowledge that has been covered by this work. Hence, this research aimed to investigate the impact of project selection criteria on organizational performance through a quantitative analysis. Based on the assessment, the general significant finding is the positive relationship between project selection criteria and organizational performance, which confirms previous studies’ findings emphasizing the crucial role of project selection criteria for individual and organizational performance. Overall, the results support the constructs and validate the positive relationships between the five project selection criteria mentioned above and organizational performance. Only the relationship between financial criteria and organizational performance was insignificant in this study. The findings of this research can aid higher-ups in construction organizations in better project selection for their organizations. This research contributes to previous literature by adding two more criteria namely environmental and management support criteria in the context of the construction industry.

Abstract

     Recent literature suggests entrepreneurs struggle to pivot—or fundamentally change aspects of their venture—due to identity-based resistance to change. Yet, when entrepreneurs receive negative feedback, overcoming this resistance may be important to pivoting their business model. We adopt a convergent, mixed methods research design to explore when and why some entrepreneurs overcome resistance to change in response to negative feedback during early-stage business model experimentation. Building upon qualitative data that we gathered and analyzed, we theorize entrepreneurs may resist pivoting their value proposition relative to other business model components despite receiving negative feedback on this aspect of their business model. However, we find three factors – entrepreneurial experience, startup mentoring, and team size – may enable entrepreneurs to pivot in response to negative feedback. We theorize that these factors broaden a startup team’s perspective, enabling value proposition pivoting during early-stage business model experimentation. We test these relationships with quantitative data from 80 startups engaged in business model experimentation and find support across hypotheses. We contribute to understanding when and why entrepreneurs pivot aspects of their business models in response to negative feedback during early-stage business model experimentation.

Introduction

     The entrepreneurship literature suggests startups may benefit from experimentation and pivoting business model components in response to negative feedback from stakeholders (Andries et al., 2021; Blank and Eckhardt, 2023; Camuffo et al., 2020; Ries, 2011). In early stages of a new venture, a business model refers to a cognitive schema of a potential activity system designed to create and capture value (Massa et al., 2017; Shepherd and Gruber, 2021; Snihur & Eisenhardt, 2022). Business model experimentation is the process of testing assumptions underlying a potential business model schema and pivoting aspects of the business model in response to negative feedback (Andries et al., 2013; McDonald and Eisenhardt, 2020; Leatherbee and Katila, 2020). Building upon prior literature, we define business model pivoting as a fundamental change to a business model component (Berends et al., 2021; Shepherd et al., 2023; Shepherd et al., 2023). Yet, startup founders often struggle to change business model assumptions in response to negative feedback. Identity-based motives to preserve beliefs and assumptions related to founders’ self-concept may conflict with the capacity to change aspects of an emerging business model in response to negative feedback (Grimes, 2018; Kirtley and O’Mahony, 2023; Zuzul and Tripsas, 2020). Despite the general understanding that founders struggle to change their ideas, however, the entrepreneurship literature currently lacks precise insight into when and why some founders can overcome resistance to pivoting distinct business model components during early-stage business model experimentation.

     In this research, we use a convergent, mixed-methods research design to explore when and why startups pivot different aspects of their business model. We do so within the context of early-stage business model experimentation, wherein startup founders explicitly state business model assumptions, test those assumptions against stakeholder feedback, and are encouraged to pivot business model components in response to negative feedback. Through an exploratory qualitative analysis in Study One, we observe that founders are more willing to pivot when negative feedback relates to peripheral aspects of their business model—the activity system designed to deliver value. By contrast, founders resist pivoting the value proposition of their business model despite receiving negative feedback from stakeholders. This finding is important because the value proposition may be critical to pivot in response to negative feedback during early-stage business model experimentation to realize value creation (Camuffo et al., 2020; Danneels, 2007; Marvel et al., 2020; Zellweger and Zenger, 2021; Zott and Amit, 2007, 2008).

Conclusion

     Our research explored how startups experiment with and pivot their business models in response to negative feedback. Using a mixed-methods research design, we highlight founders are more likely to pivot certain aspects of their business model as opposed to other aspects of their business model. To overcome this resistance to change, startups can gain entrepreneurial experience, engage frequently with startup mentors, and expand the size of their team. We suggest these factors enable pivoting because they increase breadth of perspective, which in turn allows founders to consider more alternative solutions when facing negative feedback. Overall, we contribute to understanding the social psychological mechanisms that enable startups to pivot different aspects of their emerging business model schemas during early-stage experimentation.

Abstract

     A growing body of scholarship has examined circular business models as a pathway towards sustainability. However, employee skills to support such business models have been largely overlooked. Addressing this research gap, this article proposes a comprehensive skill taxonomy for start-ups embracing circular economy transition. As the first large-N effort to develop a comprehensive skill taxonomy for circular business model implementation, this study uses a clustering analysis of self-reported skill profiles for 2407 staff working in circular start-ups. The taxonomy outlines 40 skills across six categories: business innovation, operations, social dimensions, systems, digitization, and technical issues. Findings suggest that circular business model implementation requires a set of general, sustainable, and circular skills, but some of these skills have been neglected in scholarship. Promoting circular narratives as a framing device for skill development can help advance CE towards mainstream uptake, and this study’s taxonomy offers a practical framework for using talent to accelerate CE transition.

Introduction

      Businesses and policy-makers often view the circular economy1 (CE) as a promising way to reconcile economic growth and sustainable development (Corvellec et al., 2021; Geissdoerfer et al., 2017; Kirchherr, 2022). Years into conceptual development and refinement, CE has been seen in a variety of ways ranging from holistic and comprehensive to only partially beneficial (Corvellec et al., 2021; Geissdoerfer et al., 2017) and even detrimental (Harris et al., 2021; Zink and Geyer, 2017). While the conceptual foundations of CE remain contested (Blomsma and Brennan, 2017; Korhonen et al., 2018a, 2018b; Skene, 2018), the topic is receiving growing scholarly interest (Ehrenfeld, 2004; Kirchherr and van Santen, 2019; Lüdeke-Freund and Dembek, 2017).

      Sustainability transitions research suggests that socio-technical transitions involve systemic changes in multiple dimensions beyond technology, including organizational and human decision-making. Such changes are expected to yield innovative products, services, and business models (Geels, 2004; Markard et al., 2012). CE transition is one type of socio-technical transition (Jurgilevich et al., 2016) and scholars have highlighted transition and innovation as conceptual elements of CE (Suchek et al., 2021) – both of which are seen as relevant to circular business models (CBM) (Antikainen and Valkokari, 2016; Bocken et al., 2016; Lewandowski, 2016; Santa-Maria et al., 2021). Relatedly, scholars have proposed various typologies of CBM (Henry et al., 2020; Urbinati et al., 2017) and have focused on CBM strategies, related experimentation (Bocken et al., 2016; Kane et al., 2018; Konietzko et al., 2020), and enablers and barriers around implementation (Hartley et al., 2021; Ünal et al., 2019; van Keulen and Kirchherr, 2021). While scholarship on CBM is growing (Ferasso et al., 2020; Lüdeke-Freund et al., 2019; Rosa et al., 2019), practical uptake remains limited (Centobelli et al., 2020; Kirchherr et al., 2018b; Urbinati et al., 2017).

Conclusion

     The CE concept has gained substantial momentum in the 21st century as a key facilitator of sustainability efforts. Given the influence of business decisions not only on environmental conditions but also on consumer preferences and habits, the private sector is recognized as a key catalyst for society-wide CE transition. Nevertheless, substantive progress towards CE transition remains limited. While refashioned strategies and supportive infrastructure provide businesses with some pathways, skills for CBM are an often-overlooked topic in the academic literature and in practice. A modestly sized literature offers some useful insights (e.g., Janssens et al., 2021; Sumter et al., 2021) but a systematic understanding about the relevance of employee skills to CBM implementation has yet to be fully researched or integrated into practice. This study has sought to fill this gap not only by outlining skills in circular start-ups but also by presenting a way to refine understandings about these skills and help businesses identify and cultivate them.

This study proposes the first comprehensive skill taxonomy for CBM implementation in start-ups in the literature, as far as the authors can determine. The taxonomy includes 40 skills for CBM implementation and finds that CBM implementation requires a set of general, sustainable, and circular skills. It also finds that some skills, such as digital skills, have been neglected. Skills declared as specifically circular are not as common in circular start-ups as the literature suggests. Given that CBM is not an entirely new concept, some skills identified in this study have existed in the workforce for decades. Thus, the novelty of skills for CBM implementation lies in the shifting context of their application and in their utilization as microfoundations of organizational capabilities. Circular start-ups might need to develop existing employee skills in novel or differentiated circular application contexts. Consequently, using circular narratives as a framing device for skill development can promote understanding and recognition of those skills in efforts to mainstream CE.

Abstract

     Regeneration has become a debated topic in organizational studies, yet its characteristics and distinctions from sustainability and circular business models remain unclear. This study aims to provide an initial framework for regenerative business models and differentiate them from sustainable or circular models. Employing literature reviews, six focus groups with international and indigenous participants, and comparisons with seminal articles on sustainable and circular models, this study finds that organizations with regenerative business models focus on planetary health and societal wellbeing. They create and deliver value at multiple stakeholder levels—including nature, societies, customers, suppliers and partners, shareholders and investors, and employees—through activities promoting regenerative leadership, co-creative partnerships with nature, and justice and fairness. Capturing value through multi-capital accounting, they aim for a net positive impact across all stakeholder levels. Regenerative business models share some design approaches with sustainable and circular models but differ in their main goals and systemic perspectives. Achieving regenerative business models requires strong and contested policy frameworks, including animal and nature rights and true pricing. Further research is needed to explore how organizations can incorporate intrinsic notions of value beyond capital and avoid new forms of greenwashing when adopting regeneration and net positive impact narratives.

Introduction

     The goal of sustainability has been to meet the needs of present generations without compromising the ability of future generations to meet their own needs (WCED, 1987). But the prevalence of a linear and degenerative economy has weakened the planet’s regenerative capacity, making it harder and harder to achieve this ambition (Dasgupta, 2021). Sustainability has become a necessary but insufficient condition for long-term human welfare. Next to sustaining, there is a growing need to regenerate our and the planet’s ability to meet present and future needs. This has given rise to the field and idea of regeneration.

Regeneration is a key process of biological systems and refers to “the capacity to bring into existence again” (Munoz and Branzei, 2021 ˜ , p. 509). It lets cells, organisms and ecosystems recover from and build resilience against external shocks (Carlson, 2011). The term regeneration has increasingly been used in diverse fields such as agriculture, architecture, design, energy, nature conservation or tourism, to promote healthier natural ecosystems and thriving human societies (Munoz and Branzei, ˜ 2021; Robinson & Cole, 2015; Rhodes, 2012, 2017; Hahn and Tampe, 2021; Bellato et al., 2022).

Conclusion

     This study sought to answer two research questions. First, what are regenerative business models? Second, how do regenerative differ from sustainable or circular business models? The answer to the first question is that organizations with regenerative business models focus on planetary health and societal wellbeing. They create and deliver value at multiple stakeholder levels—including nature, societies, customers, suppliers and partners, shareholders and investors, and employees—through activities promoting regenerative leadership, co-creative partnerships with nature, and justice and fairness. Capturing value through multi-capital accounting, they aim for a net positive impact across all stakeholder levels.

     The answer to the second question is that regenerative business models add to the sustainable and circular business model literature with a focus on social-ecological systems (Berkes et al., 2000), based on an understanding of the planet as a living system, the so-called Gaia hypothesis (Lovelock, 1988). They also propose the new goal and framing of planetary health and societal wellbeing and advance a motivating narrative that aims not merely at the reduction of negative impacts (net zero) or the balance between economic, social and environmental value creation (triple bottom line), but at a redefinition of the relationship between humans and nature. Regenerative business models also add a focus on individual purpose and meaning, questions of leadership and spirituality, as well as nature and animal rights and the role of indigenous communities in helping to reconnect humans and nature. The three concepts of regenerative, sustainable and circular business models have overlaps in their design foci. Regenerative and circular concepts intersect where design efforts are aimed at the regenerative biosphere. Sustainable and circular connect on the circular technosphere; and sustainable and regenerative business models connect through the sustainable humansphere. This study intends to help advance organizational efforts to evolve humanity towards planetary health and societal wellbeing.

Abstract

     This article addresses the negative implications of neutrality in bureaucratic systems and public service. Neutrality employs a sameness approach that reinforces impartiality, invisibility, and indifference, resulting in what we term discriminatory blindness. After a brief illustrative review of neutrality in public service, we critique neutrality as an organizational impediment based on its veiled negative implications and disparate outcomes that fail to center the human experience and treat people based on how they are situated to ensure equity in outcomes. We propose a framework to forge ahead with eight actionable types of initiatives and learning constructs to raise the consciousness of public practitioners. We conclude with an action-oriented and learning-focused approach.

THE PROBLEM WITH NEUTRALITY

     Neutrality fortifies organizational impediments in public service and obscures an unequal relationship, detachment from disparate outcomes, and tensions between human interactions (Gooden, 2015). Frederickson (2010) asked, “for whom is the organization well managed? For whom is the organization efficient? For whom is the organization economical? For whom are public services more or less fairly delivered?” (Frederickson, 2010, p. xv). Scholars have challenged the ideal of neutrality based on its veiled implications for vulnerable, minoritized, and marginalized communities (Blessett & Gaynor, 2021; Frederickson, 1991, 2005; Gaynor & Wilson, 2020; Gooden, 2015; Guy, 2021; Herd & Moynihan, 2018; Hooks, 2013; Portillo et al., 2020; Stivers, 2015).

Neutrality veils a bureaucratic apparatus protecting the status quo of discriminatory legislative practices (Caiden, 1996; Gooden, 2015; Huber, 1991). The veil of neutrality refers to the belief that neutrality’s evenhandedness is altruistic and a public good. The veil obscures legitimized biases, internal processes, assumptions, and disparate outcomes. The veil of neutrality is referred to in the literature as a rationalized myth that masks disparities in the equitable distribution of public services (Meyer & Rowan, 1977; Portillo et al., 2020). Hooks (2013) argued White supremacist thinking and practice remain ingrained in the political foundations and systems within the U.S. She referred to this as “imperialist White supremacist capitalist patriarchy” which demanded obedience, subservience, submissiveness, and docile participation in an interlocking system (Hooks, 2013, p. 4).

CONCLUSION

     Public administration faces a shifting paradigm that challenges neutrality as a bedrock. In this research, we adapted a framework to forge ahead with eight actionable types of cultural competence initiatives and learning constructs to mitigate disparate aspects of neutrality in public service. This article focused on the performative illusion of neutrality as an organizational learning impediment that preserves and promotes disparities, inequities, and exclusion based on the premise of objectivity. It’s time to move beyond the performative illusion of neutrality to rebalance and recalibrate public service mechanisms. To disrupt the deep entrenchment of neutrality that veils how the evenhandedness of efficiency reinforces exploitation, control, power, and dominance, it requires us to disentangle ourselves and unlearn what we have been socialized to adopt as norms.

     Public practitioners must become skilled at navigating and disarming the ubiquitous minefields of public service once boots hit the ground. Development of cultural competence requires work at the individual, collective, and organizational levels embedded in empathy, ethics, engagement, and equity (Meyer et al., 2022). Starke et al. (2018) argued it is the ethical responsibility of public administration programs to educate and prepare students to become culturally competent administrators. By default, failure to prioritize cultural competence would perpetuate dominant biased perspectives. The development of cultural competence requires reconstituting long-standing traditional modes of doing business as value hierarchies. Such a reconstitution can ensure that organizations intentionally and meaningfully engage with proposed changes and articulate an ethical commitment and responsibility to raise the consciousness of public practitioners.

Abstract

     While open innovation organizations have grown in popularity, they hold a high failure rate. This paper identifies limiting factors that contribute to this high failure rate at three levels: strategy, process, and community. After validating and expanding these limits through a case study, the paper offers a framework identifying success factors for open innovation and their hierarchical relationships. We classified these success factors into six groups, design, implementation, technology, operation, community readiness, and community development, and their relationships into four groups, deployment, engagement, evaluation, and governance. This framework and the accompanying propositions offer a better theoretical understanding of open innovation models and provide practical recommendations toward their viability, survivability, scalability, and profitability. Lastly, the paper discusses possible research avenues for the further development of open innovation organizations.

Introduction

      Open innovation (OI) is defined as a distributed innovation process based on purposively managed knowledge flows across organizational boundaries to accelerate internal innovation based on the use of external knowledge or support external innovation based on the use of internal knowledge. For a long time, the theoretical development of OI has been focused on the strategic benefits of OI in an attempt to position OI models as alternative solutions for innovation (Albats et al., 2021; Bogers et al., 2019; Chaudhary et al., 2022). While OI initiatives in tandem with digital platform technologies can potentially facilitate and enhance the innovation process and outcomes (Jesus and Jugend 2021; Nguyen et al., 2021), the downsides and the limits of this openness remain understudied (Kohler and Nickel 2017; Saura et al., 2022; Schlagwein and Bjørn-Andersen 2014; Schoder et al., 2019; Stefan et al., 2022).

     Proponents of OI have long argued that the literature falls short in documenting obstacles hindering OI’s successful implementation and governance (Bigliardi et al., 2020; Chesbrough and Brunswicker 2014; Kohler and Nickel 2017; West and Bogers 2017). Instead, previous studies have mainly articulated the general logic of OI, described the success of some well-known cases, and examined the benefits of engaging external actors in new product development (e.g. Chesbrough 2017; Corral de Zubielqui et al., 2019; Henttonen and Lehtimäki 2017; Liem et al., 2019; Xie et al., 2016). However, these success stories have generally failed to acknowledge that OI can fade in its efficacy due to many technical and non-technical reasons (Bigliardi et al., 2020).

Discussion

     Opening up the innovation process to external knowledge exploration and exploitation remains a challenging task for many organizations (Chaudhary et al., 2022; Haim Faridian and Neubaum 2021; Naqshbandi et al., 2019; Saura et al., 2022). This study synthesized the limitations of OI highlighted in recent studies and further explained their effects through a case review. Our case review also offered new insight into the relationships between these limiting factors. By classifying the identified limiting factors at the strategy, process, and community levels, we developed a framework rendering the key OI success factors and their hierarchical relationships (Fig. 2). The case study allowed us to recognize two groups of strategic decisions that could make or break OI initiatives and their design and implementation decisions. At the process level, OI success depends on both operational and technological decisions. At the community level, community readiness and community development are two critical dimensions of OI success.

Abstract

     The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.

Introduction

     The deluge of data combine with the availability of processing power and storage on digital devices has created a renewed interest in artificial intelligence (AI) in multiple fields over the past years (Enholm et al., 2021). Intense competition among organizations all over the world has also accelerated the need to deploy AI in order to gain an edge over rivals (Ransbotham et al., 2018). AI is not perceived by most C-level executives as a core competence that organizations must foster to remain competitive in the long-run (Kietzmann & Pitt, 2020). One key area of AI use within organizational operations has been B2B marketing (Mikalef et al., 2021). Intelligent solutions to augment B2B marketing capabilities are necessary in a complex business environment, as B2B operations often deal with massive informational complexity and the requirement to make quick decisions. In this regard, AI has the potential to revolutionize how conventional activities are performed due to the ability to process ever-increasing volumes of data, and provide rich insights on key business partners and customers (Bag et al., 2021). Furthermore, AI applications have been suggested to enable automation of many manual processes which can help alleviate bottlenecks and increase operational efficiency in B2B operations (Paschen et al., 2020). In fact, a recent survey on business executives conducted by Garner indicated that the majority believe that AI is likely to be a key development in their business within the next years (Shin & Kang, 2022).

Analysis

     To examine the validity and reliability of our proposed research model, we built on a partial least square based structural equation modeling (PLS-SEM) analysis. We used SmartPLS 4 as the software to run analyses (Ringle et al., 2015). For the type of analysis we conduct PLS-SEM is deemed as an appropriate technique as it allows the examination of the relationships between dependent, independent, and mediating variables (Hair et al., 2011). As PLS-SEM is a variance-based approach, it allows for (i) flexibility concerning normality, (ii) use of reflective and formative constructs, (iii) analysis of models with smaller samples, and (iv) the potential of theory building (Nair et al., 2017). Over the past years, PLS-SEM has widely been used for the analysis of models with complex relationships between constructs in several subject areas (Ahammad et al., 2017, West et al., 2016). Furthermore, PLS-SEM enables the identification of indirect and total effects, which makes it making it possible to not only simultaneously assess the relationships between multi-item constructs, but also to reduce the overall error associated with the model (Astrachan et al., 2014). The sample of 155 responses of this study surpasses both the requirements of: (1) ten times the largest number of formative indicators used to measure one construct, and (2) ten times the largest number of structural paths directed at a particular latent construct in the structural model (Hair et al., 2011). Furthermore, as the research model we examine builds on exploratory theory building instead of confirming, we consider that PLS-SEM is the best alternative.