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This study aims to investigate the relationship between leader group prototypicality and intergroup conflict, as well as its mechanisms and contextual factors using the social identity theory.

The research model was empirically tested using multi-phase, multi-source and multilevel survey data in China. The final sample consisted of 75 group leaders and 231 group members. Multilevel structural equation modelling and a Monte Carlo simulation were used for hypothesis testing.

The results showed that leader group prototypicality would engender intergroup conflict via intergroup distinctiveness. Further, leaders’ benchmarking behaviour moderated this indirect effect. In particular, leader group prototypicality resulted in higher intergroup distinctiveness and intergroup conflict, only when the leaders’ benchmarking behaviour was higher rather than lower.

First, this study addresses the question of whether leader group prototypicality would lead to intergroup conflict to provide theoretical and empirical insights to supplement extant literature. Second, the study advances the understanding of mechanisms (intergroup distinctiveness) and the consequences (intergroup conflict) of leader group prototypicality in an intergroup context. Third, the study shows that leaders’ benchmarking behaviour moderates the effect of leader group prototypicality on intergroup conflict through intergroup distinctiveness. As such, the findings are of value to future management practice by offering precise, practical interventions to manage the intergroup conflict caused by leader group prototypicality.

Cloud computing is a major enabling technology for Industry 4.0 and the Big Data era. However, cloud-based firms, who establish their businesses on cloud platforms, have received scant attention in the extant operations management (OM) literature. To narrow this gap, the purpose of this paper is to investigate cloud-based firms from an operations strategy perspective.

A two-phase multi-method approach was adopted. In the first phase, content analysis of 27 reports from cloud-based firms was conducted, aided by text mining keyword extraction. Two data-related operations capabilities were identified and hypotheses were posited regarding the relationships between data resources (DR), operations capabilities and firm growth (FG). In the second phase, a sample of 190 cloud-based firms was collected. Seemingly unrelated regression and bootstrapping method were employed to test the proposed hypotheses using the survey data.

The content analysis indicates data as a key resource and both data processing capability and data transformational capability as critical operations capabilities of cloud-based firms. FG is regarded as a top priority in the cloud context. The regression results indicate that DR and the two capabilities contribute to the growth of cloud-based firms. Moreover, a follow-up bootstrapping analysis reveals that the mediating effects of the two capabilities vary between different types of FG.

To the authors’ best knowledge, this is one of the first OM studies on cloud-based firms. This study extends the operations strategy literature by identifying and testing the key operations capabilities and priorities of cloud-based firms. It also provides insightful implications for industrial practitioners.

Currently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.

Based on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.

The results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.

Unlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

The extant literature recognizes that trade credit is influenced by the power imbalance between buyers and suppliers but most studies focus on either buyer power or supplier power. The purpose of this study is to investigate how buyer power and supplier power interact and jointly influence trade credit. Moreover, this study examines the moderating effects of political ties in an emerging economy context.

A research framework was developed by combining resource dependence theory and institutional theory to investigate the interactive effects of market power (i.e. market share and supplier concentration) and non-market power (i.e. political ties) on trade credit. The proposed hypotheses were empirically tested by a fixed effects model using secondary data from 2,433 listed firms in China.

The results show that a buyer firm's market share promotes trade credit but this effect is weakened by supplier concentration. Moreover, the buyer's political ties enhance the impact of market share on trade credit and attenuate the negative moderating effect of supplier concentration.

This study contributes to the trade credit and supply chain power literature by identifying the interactive effects of market share, supplier concentration and political ties in trade credit. It advances our understanding of how trade credit is jointly determined by a variety of factors in emerging economies.

The purpose of this paper is to synthesize the extant literature on green innovation (GI), and more importantly, to provide future directions for GI research from the innovation ecosystem (IE) perspective.

A systematic literature review is conducted to summarize the status of current GI research, followed by content analysis to identify connectivity and crossovers between key concepts in the bodies of GI and IE literature. Then, the authors propose a green innovation ecosystem (GIE) framework and offer recommendations for future research on GI.

It is evident that the bodies of GI and IE literature overlap on the roles of external actors, and yet, there is scant research investigating GI activities from the IE perspective. Moreover, there are multiple directions worthy of future research applying the proposed GIE framework.

Though the concept of ecosystem has become pervasive in innovation management research, this study is one of the first works to introduce IE concepts and propositions in the GI research context. This paper provides new knowledge on GIEs and sets the basis for further investigation.

Despite the increasingly salient role of smart manufacturing (SM) in revolutionizing operational processes, little research has explored the dynamics of corporate executives’ decision-making on SM projects after their initiation. To fill this research gap, this study examines the configurations of project complexities (i.e. technological breadth and organizational breadth) and industry conditions (i.e. industry growth and industry competition) that shape managerial decisions to complete or terminate SM projects.

Using secondary data of 125 SM projects implemented in 106 manufacturers during the 2011–2020 period, we conduct a fuzzy-set qualitative comparative analysis to empirically derive equifinal configurations that lead to the completion or termination of SM projects.

The results reveal that project complexities and industry conditions work together in configurations where manufacturers will complete or terminate SM projects. We employ prospect theory to elucidate the findings and offer propositions.

Our study extends the extant SM literature by revealing the configurations of project complexities and industry conditions that shape managerial decisions on the completion or termination of SM projects after their initiation. It contributes to the prospect theory literature by accounting for the roles of both decision content and decision context and providing empirical evidence on their joint effects on managerial risk-taking decisions that alter the subjective value and probability weight of decision outcomes.

As a popular supply chain finance (SCF) strategy, reverse factoring has been widely adopted by buyer firms. However, the extant literature provides scant empirical evidence on the performance effect of reverse factoring. The purpose of this study is to seek to narrow this gap by empirically examining the relationship between reverse factoring and operating performance and the contingency conditions of this relationship.

Based on a sample of 167 announcements of reverse factoring implementation made by publicly listed Chinese manufacturing firms between 2014 and 2018, this paper employs a long-term event study approach to analyze the operating performance effect of reverse factoring as well as the moderating effects of production and innovation capabilities.

The event study results indicate that reverse factoring has a positive effect on buyer firms' operating performance in terms of cost efficiency and operating margin. In addition, both production and innovation capabilities positively moderate the relationship between reverse factoring and operating margin. However, neither of them moderates the relationship between reverse factoring and cost efficiency.

This is the first study that empirically examines the impact of reverse factoring on operating performance based on secondary data. Furthermore, it sheds light on the SCF literature by providing insights into the contingency effects of production and innovation capabilities, which also extends our understanding of the application of extended resource-based view in SCF research.

Adopting the theory of planned behavior, this study attempts to investigate how subjective norms in the supply chain influence a firm's green innovation and how such influences are contingent upon the firm's internal and external factors (i.e. flexibility orientation and environmental dynamism). Two types of subjective norms are examined, i.e. injunctive norms (what supply chain partners say) and descriptive norms (what supply chain partners do).

Using survey data of 178 Chinese small and medium-sized manufacturers, we employ hierarchical linear regression to test the proposed hypotheses.

Our results indicate that both injunctive and descriptive norms are instrumental in stimulating firms to take actions on green innovation. Moreover, the flexibility orientation of a firm can strengthen the effect of descriptive norms. It is also observed that environmental dynamism weakens the efficacy of injunctive norms but strengthens that of descriptive norms on green innovation.

This study is among the first to apply the theory of planned behavior to explain the microfoundations of focal firm's green innovation and distinguish two types of subjective norms in the supply chain. In addition, it extends the theory of planned behavior by revealing internal and external contingent factors (i.e. flexibility orientation and environmental dynamism) of the relationship between subjective norms and firm behaviors.

Innovation ecosystem research has highlighted complementors as the critical force to determining focal firm innovation’s success in addition to the traditional value chain or supply chain perspective. However, literature is relatively scarce in terms of innovation ecosystem governance, especially, on how to manage various types of complementors. The purpose of this paper is to fill this theoretical gap by developing a typology of managing complementors from multiple case studies.

This study conducted multiple case studies of three leading focal firms with ecosystem strategies to understand innovation ecosystem governance. Theoretical themes are inductively generated to reveal their success in managing complementors in their ecosystems.

The case analysis reveals four generic strategies to manage complementors. These strategies are contingent on the types of complementors and level of interdependence: focal firms tend to engage functional complementors and collaborate with infrastructural complementors when the level of interdependence is higher, and acquire functional complementors and nurture infrastructural complementors when the level of interdependence is lower.

For practitioners, this study can improve their understanding on the mechanisms of innovation ecosystem governance, particularly interdependence between participants in an innovation ecosystem, and developing appropriate strategies to manage different types of complementors in innovation ecosystems.

This study contributes to innovation ecosystem literature by enriching the conceptualization of interdependence in innovation ecosystems and unpacking innovation ecosystem governance with the inductively developed holistic typology of strategies to manage complementors. Meanwhile, this study also suggests underlying mechanisms for how innovation ecosystem governance and, therefore, contributes to a systematic theory on understanding innovation ecosystem governance.