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Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

This article aims to explore the impact of climate disasters on small-scale farmers’ willingness to cooperate and explore the mediating effect of social capital.

The study investigates farmers’ willingness to cooperate through a framed field approach and surveys the information of individuals and villages, including climate disasters and social capital, using a structured questionnaire from rural communities in Jiangxi and Sichuan, China.

The results show that climate disasters and social capital are significant and positive determinants of farmers’ willingness to cooperate. In specific types of climate disasters, drought is positively associated with farmers’ cooperation willingness. Moreover, the mediation effect of drought on farmers’ willingness to cooperate through social capital has been demonstrated to be significant although negative, whereas the mediation effect of flood on farmers’ willingness to cooperate through social capital is significant and positive.

First, given the limited studies focusing on the impact of climate disasters on small-scale farmers’ willingness to cooperate, the authors complement the existing literature through a framed field experiment approach by designing a scenario that every farmer may encounter in their production activities. Second, the study figures out the roles of drought and flood as different kinds of climate disasters in farmers’ decision-making of cooperation and sheds light on the positive impact of climate disasters on small-scale farmers. Finally, this paper provides empirical evidence of social capital as a potential channel through which climate disasters could possibly affect farmers’ willingness to cooperate.

The purpose of this paper is to evaluate the barriers militating against the adoption of robotics in the construction industry.

Robotics implementation barriers were obtained from the previous studies and then through questionnaire survey construction stakeholders in Nigeria evaluate these barriers. Consequently, these barriers were examined via the exploratory factor analysis (EFA) technique. Furthermore, a model of these barriers was implemented by means of a partial least square structural equation modeling (PLS-SEM).

The EFA results showed that these barriers could be categorized into two: cost and technology. Results obtained from the proposed model showed that platform tools were crucial tools for implementing cloud computing.

The novelty of this research work will be provided a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of robotics.

The purpose of this study is to determine how project governance influences project success. According to the authors, such an effect is mediated by organizational support and project team cohesion. The direct and indirect effects of organizational support and project team cohesion provided helpful information. The authors’ objective is to contribute to the project management knowledge of how project team cohesion plays a significant role in project success.

Data were collected from 350 employees working in Pakistan’s oil and gas industry. Four prime oil and gas exploration companies were selected as samples based on their contribution to the revenue. SPSS v23 and AMOS v23 were used for constructing structural equation modeling and path analysis to examine the direct and indirect effects.

The results revealed that project governance is positively related to project success. Furthermore, organizational support and project team cohesion mediated the relationship between project governance and project success.

Team cohesion has been primarily a topic of interest in sports psychology literature, education and medical sciences. There is an expressed need to investigate team cohesion issues in the broad domain of organizational development, specifically the project management literature. This study contributed by discussing team cohesion in the project context. Second, project governance was investigated using the conservation of resources theory. The lens of intellectual capital was applied to examine intangible resources of project governance like rules, regulations and directives for project success.

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.

This paper aims to explore how the effect of knowledge sharing through mergers and acquisitions (M&As) on new product development (NPD) performance is contingent upon two different types of control mechanisms: behavior control and outcome control.

Leveraging the theory from transaction cost economics, this study provides answers regarding the roles of behavior and outcome controls. The hypotheses were tested empirically across a sample of 143 UK cross-border M&A firms.

The results provide the increasing call for an integrative perspective and theory in the M&A literature in that knowledge sharing through M&As is deemed decisive for NPD performance, and while both control mechanisms are effective, behavior control is more effective in enhancing NPD performance than outcome control.

The relevant M&A studies lack insights into the use of control mechanisms as a way to monitor the target firm’s behavior and performance and reduce the risk of its opportunistic behavior. Appreciating the need for M&A literature that elaborates control strategy and structure, this study incorporates behavior control and outcome control into M&A mechanisms.

This study aims to explore whether and how the two potential antecedents (i.e. relationship quality at the dyadic level and network density at the network level) affect firms’ internet-interactive capability (FIIC), referring to the capability of a specific firm to communicate and interact with the relevant partner firms on the basis of internet-interactive technologies in the internet environment and, at the same time, the following influence of FIIC on collaborative activities (i.e. joint planning and joint problem-solving).

This study designed a questionnaire and collected data on-site from 400 manufacturers. SmartPLS is used to validate the research model.

The results suggest that the dyadic relationship quality and network density of the partner group are both positively connected with a firm’s FIIC. Besides, FIIC is positively related to collaborative activities with its partners.

Given the nature of our data (i.e. cross-sectional), the authors can collect longitudinal or experimental data to retest the hypotheses.

This study gives certain guidance for firms to be aware of the factors that motivate FIIC and use their FIIC to influence their employees’ collaborative activities in their relationships with partners, thereby promoting cooperation performance.

This study attempts to extend the resource-based theory based on the logic of motivation-capability by exploring the potential antecedents of FIIC and makes contributions to the current studies on the antecedents of FIIC, which provides actionable insights for firms to play the role of FIIC in interfirm interactions.

This study aims to explore how the three types of human resource (HR) practices, encapsulated in the ability, motivation and opportunity (AMO) model, foster a learning organizational culture (LOC). In doing so, the authors evaluate the centrality of knowledge sharing (KS) in mediating this relationship.

A quantitative survey is undertaken to collect data from managers working in organizations operating in the UK. The authors use several statistical techniques to assess the psychometric properties of the measures and test the hypotheses using multiple regression executed with Preacher and Hayes’ Process macro.

The findings show that the AMO HR practices significantly facilitate the development of a LOC in the workplace, and KS among organizational members amplifies the effects of these HR practices in the process.

A LOC functions as an important source of organizational performance and effectiveness. It enhances the absorptive capacity of the organization to capture, share and transfer knowledge to optimize work. Hence, developing a culture that nurtures organizational learning could be a priority for managing HR. This study, therefore, extends the understanding of the role of AMO HR practices in fostering a learning culture – thus, providing managers with the essential knowledge to improve performance. The study also enriches the literature on HR practices, KS and LOC by integrating these three variables into a unifying framework.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.