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Society's concerns about environmental degradation have tightened competitive pressure and brought new challenges to small firms. Against this backdrop, this study develops a decision model to determine a suitable configuration for entrepreneurial orientation to help small firms manage circular economy challenges and improve their performance.

This study used a multi-study and multi-method approach. Study 1, through qualitative in-depth interviews, identified a portfolio of circular economy challenges and entrepreneurial-orientation components. Study 2 applied the quality function deployment technique to determine the most important components of entrepreneurial orientation. Study 3 adopted a fuzzy set qualitative comparative analysis to determine the best configuration for challenges and components.

The findings reveal a set of challenges and identify the salient need to combine the negation of these challenges with the components of entrepreneurial orientation; this combination will improve the performance of small firms. The research extends the current knowledge of managing circular economy challenges and offers decision-makers insights into improving their resilience.

The use of the dynamic capability view, together with the multi-study and multi-method approach, may lead to an appropriate reconfiguration of entrepreneurial orientation, which, to date, has received limited empirical attention in the small-business-management discipline.

The present study investigates human behavioral and emotional experiences based on human-built environment interaction with a specific interest in urban graffiti displaying fear and pleasure-inducing facial expressions. Regarding human behavioral and emotional experience, two questions are asked for the outcome of human responses and two hypotheses are formulated. H1 is based on the behavioral experience and posits that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified behavioral fear and pleasure responses. H2 is based on emotional experience and states that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified emotional fear and pleasure responses.

The research design is developed as a multi-method approach, applying a lab-based experimental strategy (N:39). The research equipment includes a mobile electroencephalogram (EEG) and a Virtual Reality (VR) headset. The behavioral and emotional human responses concerning the representational features of urban graffiti are assessed objectively by measuring physiological variables, EEG signals and subjectively by behavioral variables, systematic behavioral observation and self-report variables, Self-assessment Manikin (SAM) questionnaire. Additionally, correlational analyses between behavioral and emotional results are performed.

The findings of behavioral and emotional evaluations and correlational results show that specialized fear and pleasure response patterns occur due to the affective characteristics of the urban graffiti's representational features, supporting our hypotheses. As a result, the characteristics of behavioral fear and pleasure response and emotional fear and pleasure response are identified.

The present paper contributes to the literature on human-built environment interactions by using physiological, behavioral and self-report measurements as indicators of human behavioral and emotional experiences. Additionally, the literature on urban graffiti is expanded by studying the representational features of urban graffiti as a parameter of investigating human experience in the built environment.

The existing literature reflects that the connection between enablers of Industry 4.0 (I4.0), Supply Chain (SC) sustainability and reliability is understudied. To cover this gap, the purpose of this study is to identify and benchmark the enablers of I4.0 for SC sustainability to build a Reliable Supply Chain (RSC).

This study benchmarks the I4.0 enablers for SC sustainability for building a RSC and analyses them with a multi-method approach. The identified potential enablers are validated empirically. A multi-method approach of Analytical Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-II) was used to investigate the influence of the identified benchmarking enablers and develop an interrelationship diagram among the identified enablers.

This study benchmarks the potential enablers of I4.0 to achieve high ecological-economic-social gains in SCs considering the Indian scenario. Digitalization of the supply chain, decentralization, smart factory technologies and data security and handling are the most prominent enablers of I4.0 for SC sustainability to build a RSC.

The findings from the study may benefit managers, practitioners, specialists, researchers and policymakers interested in I4.0 sustainability applications.

Due to land resource scarcity, sustainable urban development in high-density cities has long been challenging. As such, many cities are formulating plans to “dig deep”, resulting in more citizens working and/or staying underground for longer periods of time. However, owing to the particularities of underground space, the factors involved in the creation of a healthy environment are different from those involved in aboveground developments. This study thus aims to investigate the influences of various underground environment factors on users' health through a holistic approach.

To achieve this aim, 12 underground sites and 12 corresponding aboveground sites are selected for a large-scale questionnaire survey, resulting in 651 survey samples. The survey covers post-occupancy evaluation of health (physical and psychosocial), underground environmental quality (visual, thermal, acoustic comfort, indoor air quality and ventilation), space design and greenery. Independent-sample T-test, Pearson correlation, multiple regression modelling and structural equation modelling are used to investigate whether significant differences exist between health of underground and aboveground users, and to develop an underground environment-health model for unveiling the significant associations between underground environment factors and users' health. To cross validate the results, an objective field measurement study is further conducted on six underground sites. The objective measurement results are used to cross validate the survey results.

The questionnaire results provide the following evidence: (1) health of underground users is significantly poorer than that of their aboveground counterparts; (2) underground development users' health is significantly affected by space design, greenery and environmental quality in terms of thermal comfort, indoor air quality, ventilation and acoustic comfort but not visual comfort; and (3) amongst the various identified factors, space design has the strongest predicting effects on human health. The field study echoes the survey findings and further unveils the relationships between different environmental factors and human health.

The results shed light on the importance of distinguishing between underground developments and aboveground ones in various guidelines and standards, especially those related to space management.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

This paper examines how different configurations of societal impact are pursued by purpose-driven organizations (PDOs) and how these configurations align with the application of varying supply chain design (SCD) practices.

This multi-method study uses quantitative data from 1588 B Corps and qualitative data from 316 B Corps to examine how PDOs align SCD with the pursuit of diverse types of societal impact. The authors first conduct a cluster analysis to group organizations based on the impact they create. Second, qualitative content analysis connects impact with enabling SCD elements.

The analysis of the five identified clusters provides detailed empirical insights on influencers, design decisions and building blocks adopted by PDOs to drive a range of societal impacts. Specifically, the nature of the impact pursued affects (1) whether a PDO will be more influenced by a need in the political environment or an opportunity in the industry environment, (2) the relative importance of the design of social flows versus material flows and (3) the need to develop new relational resources with beneficiaries versus leveraging existing capabilities to manage inter-firm processes.

This study responds to calls to disaggregate different dimensions of societal impact and examines the relationship between SCD and a breadth of sustainability impacts for different stakeholders. In doing so, the authors identify four SCD pathways organizations can follow to achieve specific societal impacts. This study is also the first to employ a supply chain perspective in the study of certified B Corps.

This study responds to the calls from the literature on identifying interactions among the sustainable supply chain management (SSCM) drivers, which influence focal firms’ SSCM decisions. It also determines how the effect of SSCM drivers differs across the upstream and downstream supply chains (SCs) entities of fast-moving consumer goods (FMCGs) companies.

We employ a multi-method design encompassing three studies: study 1 (multiple-case study), study 2 (quantitative survey), and study 3 (multiple-criteria decision-making or MCDM modeling).

The results show that the external drivers such as customer pressure, competition, and supplier pressure and internal drivers such as top management commitment interact to influence the adoption of SSCM practices, and this interaction is diverse across upstream and downstream SC entities of the FMCG sector. The study provides empirical evidence of relationships among the SSCM drivers, which influence SSCM decisions.

Understanding these interactions will help managers derive strategies to manage the overall SSCM ecosystem and recognize the multiplier effects of upstream to downstream and vice versa.

This study aims to conceptualize and measure strategic leadership capabilities within research and development (R&D) teams pursuing high-tech innovation at public-funded R&D organizations in India.

A rigorous five-stage multi-method approach defines, conceptualizes and validates the core construct “strategic leadership capabilities for innovation” (SLCI). The first stage correlates the insights generated from theoretical analysis and expert opinions on the importance of leadership for innovation. The second study identifies a three-dimensional factor structure underlying the SLCI construct and the third validates it through a confirmatory factor analysis. Replication provides additional validation.

SLCI emerges as a three-dimensional construct with sub-dimensions: dynamic envisioning, ambidextrous resource utilization and empowering support for innovation.

Conceptualization of the SLCI construct and its measurement through a 15-item scale that has been empirically validated in the public-funded R&D organizations in India.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

In parallel with the rising importance of innovation and digital technologies, research on platform ecosystems is rapidly accumulating. This study aims to summarize the current research published in leading technical and innovation management (TIM) journals and provide recommendation for future research.

In this paper, the authors combine multiple quantitative literature review methods (social network analysis, citation analysis and co-citation analysis) with a systematic literature review of articles published in the 13 most influential TIM journals over a 15-year period.

The citation network of 168 selected papers is sparse, with low network density indicating the emerging nature of the field. Using network centrality values, the authors identified the 33 most influential articles. The review of methodologies in these articles found that conceptual and case study research dominate, suggesting the need for additional confirmatory and quantitative analysis. Co-citation analysis of the references identified six research clusters: foundation, network-centric innovation, complementor management, platform competition, ecosystemsand product platforms.

This analysis is among the first to examine the knowledge structure of platform ecosystem research in the TIM domain by using multiple evidence-based analysis methods. The authors also apply the theory, context, characteristics and methodology framework to suggest areas for future research.