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This paper aims to reveal how different types of events and top management teams' (TMTs’) cognitive frames affect the generation of breakthrough innovations.

Drawing on the event system theory and upper echelon theory, this study chose a Chinese manufacturing enterprise as the case firm and conducted an exploratory single-case study to unpack how breakthrough innovation generates over time.

By conducting the in-depth case analysis, the study revealed that firms do not produce breakthrough innovation in the catch-up stage and parallel-running stage but achieve it in the leading stage. It also indicated that when facing proactive events in the catch-up stage, TMTs often adopt a contracted lens, being manifested as consistency orientation, less elastic organizational identity and narrower competitive boundaries. In addition, they tend to adopt a contracted lens when facing reactive and proactive events in the parallel-running stage. In the face of reactive and proactive events in the leading stage, they are more inclined to adopt an expanded lens, being manifested as a coexistence orientation, more elastic organizational identity and wider competitive boundaries.

First, by untangling how TMT's cognitive frame functions in breakthrough innovations, this paper provides a micro-foundation for producing breakthrough innovations and deepens the understanding of upper echelon theory by considering the cognitive dimension of TMTs. Second, by teasing out several typical events experienced by the firm, this paper is the first attempt to reveal how events affect the generation of breakthrough innovation. Third, the work extends the application of the event system theory in technological innovation. It also provides insightful implications for promoting breakthrough innovations by considering the role of proactive and reactive events a firm experiences and TMT's perceptions.

Drawing on the upper echelons theory, this study focuses on how top management team (TMT) heterogeneity affects breakthrough innovations and examines how strategic decision-making logic (including causation and effectuation) moderates the relationship between TMT heterogeneity and breakthrough innovation.

By conducting an empirical test of 227 sample firms in China, the authors applied linear hierarchical regression analysis to test the hypotheses on the TMT heterogeneityinnovation relationship and the moderating roles of causation and effectuation.

The empirical tests show that TMT heterogeneity positively affects breakthrough innovation, and both causation and effectuation positively moderate the positive relationship between TMT heterogeneity and breakthrough innovation. In addition, effectuation has a stronger moderating effect on the positive correlation between TMT heterogeneity and breakthrough innovation than causation.

This study extends the upper echelons theory to explain how the characteristics of TMTs affect firm innovation. Specifically, the authors explore the TMT heterogeneity–breakthrough innovation relationship from the perspectives of information processing and core competence and reveal the boundary condition of strategic decision-making logic in the correlation between TMT heterogeneity and breakthrough innovation. In this vein, the authors contribute to the literature by untangling the internal mechanisms between TMT heterogeneity and breakthrough innovation and extending the discussion on effectuation theory from the entrepreneurship domain to the innovation field. Furthermore, the research findings can provide helpful implications for TMTs to manage breakthrough innovation effectively.

With the growth in online purchasing, the return of distressed shipments also increased. The return experience of the online shopper has a huge impact on their next purchase decision-making. This explanatory research aims to identify and empirically explain factors related to the online buyer’s return experience that influence the repurchase intention of young buyers.

Primary data were collected from 235 active online young buyers who have experienced returning the goods through a structured questionnaire. Structural equation modeling is used for analyzing the data.

This study reveals that an online return policy leniency strongly supports service recovery quality, expected return convenience, buyer trust and satisfaction, which lead to repurchase intentions. Moreover, return satisfaction positively impacts repurchase intention while mediating young buyer trust.

This study is one of the few relevant pieces of research that would benefit e-tailers to improve their product return policy and compel young buyers’ intention to make a repeat purchase.

A crucial question still remains unanswered as to whether data analytics-oriented business intelligence (hereafter, BI) technologies can bring organizational value and benefits. Thereby, several researchers called for further empirical research to extend the limited knowledge in this critical area. In an attempt to deal with this issue, we presented and tested a theoretical model to assess BI effectiveness at the organizational benefits level in this research article.

The suggested research model expands the application of the DeLone and McLean model in BI technology success or effectiveness research from individual level to organizational level. A cross-sectional survey is developed to obtain primary quantitative data from business and technology managers who are depending on BI technologies to make operational, technical and strategic decisions in Jordanian-listed firms.

Empirical findings show that system quality, information quality and training quality are significant predictors of user satisfaction, but not of perceived benefit. Data quality was found to be a strong predictor of both perceived benefit and user satisfaction. The influence of perceived benefit on user satisfaction was significant in turn both factors positively affect organizational benefits.

This research paper is a pioneering effort to assess BI technology effectiveness at an organizational level outside the context of developed countries. To the best of the authors’ knowledge, no prior research has combined all dimensions used in this research in one single model.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

This study aims to investigate the intervening role of lean management (LM) in the direct relationships between green market orientation, green value-based innovation, green reputation and enterprise social performance.

Data for the study is carefully garnered from 217 managers in Ghanaian small- and medium-sized enterprises. The methodological technique used to validate all hypothesized relationships is partial least squares structural equation modelling.

The empirical results of the study suggest that although green market orientation has a positive impact on green value-based innovation, the effect is not significant. However, the results confirm that green market orientation has a significant positive impact on green reputation and enterprise social performance. The results further suggest that LM has a significant positive impact on green value-based innovation, green reputation and enterprise social performance. The mediation analysis provides empirical evidence to suggest that LM fully mediates the relationship between green market orientation and green value-based innovation. Lastly, the results of the mediation analysis suggest that LM plays a complementary partial mediation role between green market orientation, green reputation and enterprise social performance.

Despite the flourishing research on green market orientation in marketing management and environmental literature, no study has been carried out to explore the intervening role of LM in the relationships between green market orientation, green value-based innovation, green reputation and enterprise social performance. Thus, considering LM as a missing link between green market orientation, green value-based innovation, green reputation and enterprise social performance is a noteworthy research gap which this study fills.