Essential Micro‐foundations for Contemporary Business Operations: Top Management Tangible Competencies,Relationship‐based Business Networks and Environmental Sustainability

Although various studies have emphasized linkages between firm competencies, networks and sustainability at organizational level, the links between top management tangible competencies (TMTCs) (e.g. contemporary relevant quantitative‐focused education such as big data analytics and data‐driven applications linked with the internet of things, relevant experience and analytical business applications), relationship‐based business networks (RBNs) and environmental sustainability have not been well established at micro‐level, and there is a literature gap in terms of investigating these relationships. This study examines these links based on the unique data collected from 175 top management representatives (chief executive officers and managing directors) working in food import and export firms headquartered in the UK and New Zealand. Our results from structural equation modelling indicate that TMTCs are the key determinants for building RBNs, mediating the correlation between TMTCs and environmental sustainability. Directly, the competencies also play a vital role towards environmental practices. The findings further depict that relationship‐oriented firms perform better compared to those which focus less on such networks. Consequently, our findings provide a deeper understanding of the micro‐foundations of environmental sustainability based on TMTCs rooted in the resource‐based view and RBNs entrenched in social network theory. We discuss the theoretical and practical implications of our findings, and we provide suggestions for future research.     

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory,Resource‐Based View and Big Data Culture

The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.     

Data‐Driven Classification Using Boundary Observations

Classification is often a critical task for business managers in their decision‐making processes. It is generally more difficult for a classification scheme to produce accurate results when the input domains of the various output classes coincide, to some degree, with one another. In an attempt to address this issue, this article discusses a data‐driven algorithm that identifies the region of coincidence, or overlap, for two‐group classification problems by empirically determining the convex boundary for each group. The results are extendable to multigroup classification. The class membership of a new observation is then determined by its relative position with respect to each of these boundaries. Due to minimal data storage requirements, this boundary‐point classification technique can adapt to changing conditions far more easily than other approaches. Test results demonstrate that the new classification technique has similar performance to a back‐propagation neural network under static conditions and significantly outperforms a back‐propagation neural network under dynamic conditions.     

Market Orientation,Ownership Type,and E‐Business Assimilation: Evidence from Chinese Firms

While more and more firms have implemented e‐business in business operations, a better understanding of the factors that successfully drive the assimilation of e‐business will provide insights for firm executives and practitioners to develop effective strategies for e‐business. Different from previous studies that focus on individual‐level factors related to business executives and top management teams, this study examines how firm‐level strategic and cultural factors shape e‐business assimilation. Based on the strategy and marketing literature on market orientation and firm ownership, we developed a research model to describe how a firm's market orientation impacts e‐business assimilation. The model also describes the moderating effect of firm ownership type on the relationship between market orientation and e‐business assimilation. Based on data from 301 Chinese international trade firms, we found that two dimensions of market orientation (i.e., customer orientation, competitor orientation) had significant effects on e‐business assimilation. However, the third dimension, interfunctional coordination, was only partially significant. In addition, ownership type was a significant moderator of the effects of customer orientation and competitor orientation on e‐business assimilation, although ownership type was not a moderator of interfunctional coordination. Being one of the first studies of the impact of market orientation and firm ownership type on e‐business assimilation, we conclude with a discussion of the implications for future research and practice.     

Data‐Driven Supply Chain Orientation and Financial Performance: The Moderating Effect of Innovation‐Focused Complementary Assets

Drawing on the complementary assets framework, this study explores the moderating effect of innovation‐focused complementary assets (CA‐I) on the relationship between a data‐driven supply chain orientation (DDSCO) and firm financial performance. To test the moderating effect, survey data gathered from 329 manufacturing firms in China were analysed using a moderated regression analysis. The results indicate that DDSCO has a significant positive effect on financial performance, and that capabilities for product and process innovation function as complementary assets moderating the DDSCO–performance relationship. The findings suggest that innovation‐focused complementary assets are performance differentiators when paired with a DDSCO and explain why some firms obtain financial benefits from the development of a DDSCO while others do not. Specifically, competitive advantage from a DDSCO may not be realized unless CA‐I and potentially other complementary assets are harnessed, thus providing useful practical guidance to managers. Hence, the study provides empirical support for the complementary assets framework.     

Transaction‐Specific Investments,Relational Norms,and ERP Customer Satisfaction: A Mediation Analysis*

Integrating the perspectives of transaction cost economics, the resource‐based view, and resource dependency theory, this study analyzes the institutional settings of enterprise resource planning (ERP) implementations in China. Specifically, it examines how bilateral transaction‐specific investments (TSIs) and relational governance mechanisms influence customer satisfaction with ERP implementations. The model is empirically tested using data from on‐site interviews with 208 ERP customers in China. The results demonstrate that the effects of vendors’ and customers’ TSIs on customer satisfaction are facilitated by multiple‐stage micromediational chains. The influence of TSIs on customer satisfaction is mediated by relational norms, and the impact of relational norms on customer satisfaction is bridged by perceived service quality and customer trust. Furthermore, the influence of vendors’ TSIs is stronger than the influence of customers’ TSIs. The findings contribute to business research and practice by providing valuable insights into how ERP vendors and customers should strategize TSIs to enhance relationship performance.     

Enterprise Resource Planning (ERP) Competence Constructs: Two‐Stage Multi‐Item Scale Development and Validation*

This paper defines and operationalizes eight ERP competence constructs. We define ERP competence as a portfolio of managerial, technical and organizational skills and expertise posited as antecedents to improved business performance occurring after an ERP system is operational and functionally stable. To improve responses to changes in markets and products, manufacturers are increasingly adopting ERP systems. However, anecdotal accounts indicate that the realization of ERP's potential benefits is rare. Because of its pervasive influence on manufacturing and business performance, the need for scientifically developed and tested multi‐item scales pertaining to ERP competence is highly relevant to manufacturing strategy research. We follow a two‐stage normative process of scale development. First, we identify a portfolio of eight generic constructs that are hypothesized to be associated with successful ERP adoption. Each construct is then operationalized as a multi‐item measurement scale by applying a manual item sorting technique iteratively to independent panels of expert judges until tentative reliability and validity is established. Second, we further refine and validate the multi‐item scales using survey data from 79 North American manufacturing users of ERP systems.     

大数据环境下的决策范式转变与使能创新

大数据的迅猛发展对社会、经济和人们的生活产生了巨大影响,也冲击和正在重塑着管理决策和价值创造的过程和方式。本文基于一系列的研究,从范式和创新角度凝练和阐述了大数据引发的重要转变和使能作用。一方面,传统的决策范式在大数据环境下遇到了深刻挑战,使得信息情境、决策主体、理念假设、方法流程等决策要素呈现出显著变化,催生了新型的大数据决策范式;另一方面,在大数据决策范式下,大数据使能进一步激发了行为洞察、风险预见和业务模式等方面的创新。     

Managing Nationality Diversity: The Interactive Effect of Leaders’ Cultural Intelligence and Task Interdependence

In light of the workforce's increasing nationality diversity, our study explores the antecedents for the successful management of nationality diversity as visualized in a favourable diversity climate and enhanced team performance. We propose a double‐contingency model in which we argue that the effects of nationality diversity will be dependent upon task interdependence and leaders’ cultural intelligence. We propose that nationality diversity will be more consequential in more interdependent teams, in which team interactions and processes are more salient. Moreover, team leaders with higher cultural intelligence will possess the skills to foster adequate team processes and thereby enhance diversity climate and performance of nationally diverse, more interdependent teams. We collected multi‐source data from 63 work teams (N = 410) and their supervisors at a German facility management company. Moderated regression analyses supported the hypothesized three‐way interaction between nationality diversity, task interdependence and leaders’ cultural intelligence. Additional simple slope analysis showed that nationality diversity is positively related to diversity climate and performance only when both team leaders’ cultural intelligence and task interdependence are high. Our study not only provides recommendations for successful nationality diversity management but also yields theoretical implications for diversity and cultural intelligence research.     

Path to “Stardom” in Globally Distributed Hybrid Teams: An Examination of a Knowledge‐Centered Perspective using Social Network Analysis

Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups.     

Adaptive versus Proactive Behavior in Service Recovery: The Role of Self‐Managing Teams

In this article, we develop a conceptual model of adaptive versus proactive recovery behavior by self‐managing teams (SMTs) in service recovery operations. To empirically test the conceptual model a combination of bank employee, customer, and archival data is collected. The results demonstrate support for independent group‐level effects of intrateam support on adaptive and proactive recovery behavior, indicating that perceptual consensus within service teams has incremental value in explaining service recovery performance. In addition, we provide evidence that adaptive and proactive recovery behavior have differential effects on external performance measures. More specifically, higher levels of adaptive performance positively influence customer‐based parameters (i.e., service recovery satisfaction and loyalty intentions), while employee proactive recovery behavior contributes to higher share of customer rates.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Team‐specific Human Capital and Performance

In this paper we explore the effects of team‐specific human capital (TSHC) on performance. We do so by delineating between two dimensions of TSHC, relating to team members and the team manager, and then exploring how the two dimensions may interact in shaping performance. Employing a 10‐year panel of football teams from the English Premier League we find that team members’ TSHC has a positive and significant effect on team performance, which is positively moderated by managers’ TSHC. Our results attest to the importance of considering both the team member and team manager dimensions of TSHC, and how the performance effects of team members’ TSHC are shaped by managers’ TSHC. Our results stand in stark contrast to the dramatic reduction in managerial tenure that has characterized the English Premier League in recent years.     

Customer Learning Processes,Strategy Selection,and Performance in Business‐to‐Business Service Firms*

Learning about customers takes place through relevant dialogues with those customers, also known as customer relationship management (CRM). As relationships develop, information about the customer is gathered in the firm's customer information systems (CIS): the content, processes, and assets associated with gathering and moving customer information throughout the firm. This research develops a measure of CIS management capabilities based on learning organization theory and measured by the ability to get, store, move, and use information throughout the business unit. This measure is then used to analyze customer learning processes and associated performance in the context of marketing strategic decision making. This study of 209 business services firms finds that generic marketing strategy positioning (low‐cost and differentiation) and the marketing tactics of personalization and customization are related to CIS development. Customer information systems development in turn is associated with higher levels of customer‐based performance, which in turn is associated with increased business growth. Since the strongest association with customer‐based performance is strategy selection, the long‐term benefits of the knowledge gained from the CIS may be in the ability to assist in measuring customer‐based performance, rather than in the ability to immediately contribute to performance. Finally, for these firms, customization and personalization are not directly associated with performance and thus may not be necessary to support every firm's marketing strategy.     

Multilevel Social Dynamics Considerations for Project Management Decision Makers: Antecedents and Implications of Group Member Tie Development

Successful projects represent the effective culmination of management skills, planning, and individual project member strengths. In operations management, such strengths are often viewed predominantly from the perspective of skill base. However, it has become increasingly evident that behavioral traits associated with individuals play a very significant, if not ultimately dominating, role in the effectiveness of certain group projects. Our aim in this study is to look into how certain individual attributes viewed as relevant to these project contexts may lead to social networking decisions that have impacts spanning multiple levels of analysis. Such insights are likely to prove valuable to decision makers managing project teams as well. We employ a controlled 4‐month investigation of multiple projects, for which we are able to consider both objective, and subjective pre‐, in situ, and postproject data. Our results demonstrate that the issues of perceived control, confidence, and conscientiousness are relevant not only in driving individual perceptions of the value of within‐group interactions, and hence the development of associated ties, but are also ultimately relevant in helping to drive higher levels of group performance.     

Critical Business Ethics: From Corporate Self‐interest to the Glorification of the Sovereign Pater

Research in critical business ethics has demonstrated how economic self‐interest is the primary reason that businesses adopt nominally ethical practices. After reviewing this body of research, the authors propose that it can be further developed by questioning its conception of self‐interest, by exploring its non‐economic dimensions and by reconsidering the meaning of the ‘self’ that is said to have such interests. Drawing insights from feminist theory and political theology, the paper interrogates corporate business ethics as a public glorification of corporate power based on a patriarchal conception of the corporation. Genealogically rooted in early Christian ceremonial practices used to glorify God the Father, this is a glorification for the sake of glory rather than just for the sake of commercial ends. The authors further argue that corporate business ethics is rendered as the feminized servant of the sovereign corporate patriarch, always at hand to glorify the master. The meaning of corporate business ethics is hence one where the feminine is not absent, but rather is servile to a masculinity conceived in relation to domination, greatness and sovereignty. Collectively, this shows how the power wielded and desired by corporate business ethics far exceeds the pursuit of financial self‐interest; it is also related to modelling the corporation on a male God. The paper concludes by considering how research in critical business ethics can be extended through forms of inquiry that destabilize the ethical glorification of the corporation, and displace its masculinist privilege.     

Confidence Matters: Self‐efficacy Moderates the Credit that Supervisors Give to Adaptive and Proactive Role Behaviours

In complex and uncertain work environments, employees need to not only be proficient in carrying out their core duties, but also to be adaptive (able to cope and respond to unpredictable events) and proactive (able to anticipate the situation and act in a self‐directed way) in their work roles. In this study we investigate the extent to which supervisors actually give credit to adaptive and proactive role behaviours when they judge employees’ overall job performance. Drawing on attribution theory, we propose that the extent to which these role behaviours are valued by supervisors will be enhanced by employees’ confidence for relevant role behaviours. Support for these ideas is provided using data from junior doctors and their supervisors in a hospital emergency department. Adaptive role behaviours positively influenced supervisors’ judgements of overall job performance. This relationship was stronger for employees with high self‐efficacy for achieving outcomes. Engaging in proactive role behaviours while also lacking role‐breadth self‐efficacy resulted in supervisors’ giving employees less credit for their proactive role behaviours. Findings support the argument that employees’ self‐efficacy for specific role behaviours provides attributional cues about capability that modify how adaptive and proactive role behaviours are interpreted and valued.     

e‐Integration in the Supply Chain: Barriers and Performance*

Current opinion holds that Internet‐based supply chain integration with upstream suppliers and downstream customers (called “e‐integration” in this paper) is superior to traditional ways of doing business. This proposition remains untested, however, and similarly we know little about what are the upstream, internal, and downstream barriers to implementing e‐integration. This paper empirically addressed these questions using data from a large single nation study, and found (1) a positive link between e‐integration and performance, and (2) that internal barriers impeded e‐integration more than either upstream supplier barriers or downstream customer barriers. Findings from this study contribute to our theoretical understanding of implementing change in contemporary supply chains, and have important implications for manufacturers interested in improving their supply chain's performance using the Internet.     

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective

Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource‐based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy‐set qualitative comparative analysis to analyse multi‐source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations.     

Traffic‐Based Labor Planning in Retail Stores

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.