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This paper aims to explore the role of human resource (HR) analytics on employees’ willingness to improve performance. In doing so, the paper examines issues related to the performance appraisal (PA) system which affect employees’ willingness to improve performance and how HR analytics can be a potential solution to deal with such issues.

The paper develops a conceptual framework along with propositions by integrating both academic and practitioner literatures, in the field of HR analytics and performance management.

The paper proposes that the use of HR analytics will be negatively related to subjectivity bias in the PA system, thereby positively affecting employees’ perceived accuracy and fairness. This further positively affects employees’ satisfaction with the PA system, which subsequently increases employees’ willingness to improve performance.

The paper provides implications for both researchers and practitioners in the performance management area for improving employees’ performance by applying HR analytics as a strategic tool in the PA system. It also provides implications for future researchers to empirically test the conceptual framework in different organizational settings.

The paper offers insights into how the use of HR analytics can deal with issues of subjectivity bias in the PA system and positively affects employees’ willingness to improve performance.

Big data analytics (BDA) helps service providers with customer insights and competitive information. It also empowers customers with insights about the relative merits of competing services. The purpose of this paper is to address the research question, “How does big data analytics enable frontline employees (FLEs) in effective service delivery?”

The research develops schemas to visualise service contexts that potentially benefit from BDA, based on the literature drawn from BDA and FLEs streams.

The business drivers for BDA and its level of maturity vary across firms. The primary thrust for BDA is to gain customer insights, resource optimisation and efficient operations. Innovative FLEs operating in knowledge intensive and customisable settings may realise greater value co-creation.

There exists a considerable knowledge gap in enabling the FLEs with BDA tools. Managers need to train, orient and empower FLEs to collaborate and create value with customer interactions. Service-dominant logic posits that skill asymmetry is the reason for service. So, providers need to enhance skill levels of FLEs continually. Providers also need to focus on market sensing and customer linking abilities of FLEs.

Both firms and customers need to be aware of privacy and ethical concerns associated with BDA.

Knitting the BDA and FLEs research streams, the paper analyses the impact of BDA on service. The research by developing service typology portrays its interplay with the typologies of FLEs and BDA. The framework portrays the service contexts in which BD has major impact. Looking further into the future, the discussion raises prominent questions for the discipline.

The purpose of this paper is to develop a framework and propositions to advance research and practice in sustainable supply chain management (SSCM). Performance indicators (economic, environmental, and social) identified in the paper aim to facilitate empirical testing of a range of theoretical models derived or extended from the stated propositions.

The study of SSCM is truly complicated, and there is no one theory that applies in all instances. The authors analyzed over 200 critical articles published in major supply chain management and sustainability-based journals and grounded the proposed framework in a multi-theoretical perspective.

SSCM implementation entails linking stakeholder pressures, moral motives, and management commitment with relational practices. The paper further elucidates how relational practices, when bundled together, can create a set of relational capabilities, which in turn transform stakeholder pressures into sustainable outcomes.

The research framework contributes to SSCM theory building insofar as it can be expanded into various theoretical models, allowing researchers to empirically test the links among SSCM drivers, management commitment, and relational capabilities, along with their individual or collective impact on supply chain performance.

The framework provides a roadmap for firms to develop and nurture relational capabilities while dealing with growing stakeholder pressures. Moral motives strengthen top management commitment, which helps channel stakeholder pressures toward the proactive development of relational capabilities.

The paper fulfills a call for utilizing multiple theoretical lenses to examine complex SSCM phenomena and, ultimately, to create a coherent theory of SSCM.

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.

Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.

Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.

Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.

This study provides useful management insights into the effective use of BA for improving organizational performance.

Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance.

The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience.

The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance.

The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model.

The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

Big data analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. The purpose of this paper is to explore information quality dynamics in big data environment linking business value, user satisfaction and firm performance.

Drawing on the appraisal-emotional response-coping framework, the authors propose a theory on information quality dynamics that helps in achieving business value, user satisfaction and firm performance with big data strategy and implementation. Information quality from BDA is conceptualized as the antecedent to the emotional response (e.g. value and satisfaction) and coping (performance). Proposed information quality dynamics are tested using data collected from 302 business analysts across various organizations in France and the USA.

The findings suggest that information quality in BDA reflects four significant dimensions: completeness, currency, format and accuracy. The overall information quality has significant, positive impact on firm performance which is mediated by business value (e.g. transactional, strategic and transformational) and user satisfaction.

On the one hand, this paper shows how to operationalize information quality, business value, satisfaction and firm performance in BDA using PLS-SEM. On the other hand, it proposes an REBUS-PLS algorithm to automatically detect three groups of users sharing the same behaviors when determining the information quality perceptions of BDA.

The study offers a set of determinants for information quality and business value in BDA projects, in order to support managers in their decision to enhance user satisfaction and firm performance.

The paper extends big data literature by offering an appraisal-emotional response-coping framework that is well fitted for information quality modeling on firm performance. The methodological novelty lies in embracing REBUS-PLS to handle unobserved heterogeneity in the sample.

Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important drivers of marketing performance and that marketing analytics use (MAU) enables firms to gain valuable knowledge and insights for improving firm performance. However, there has been little focus on how firms improve their CRM and brand management via MAU. This study aims to draw on the absorptive capacity theory, research on marketing capabilities and marketing analytics to examine the capability-developing mechanisms that enable a firm to use marketing analytics to enhance its CRM and brand management capabilities, thereby improving its marketing performance.

A research model is developed and tested based on an analysis of 289 responses collected using an online survey from middle and senior managers of Chinese firms with sufficient knowledge and experience in using marketing analytics for survey participation.

The findings demonstrate that MAU is positively related to both CRM and brand management capabilities, which in turn are positively associated with marketing performance; and that both CRM and brand management capabilities mediate the relationship between MAU and marketing performance.

The study’s outcomes were based on data collected from a survey, which was distributed using mass e-mails. Thus, the study is unable to provide a meaningful response rate. The research results are based on and limited to Chinese firms.

MAU is essential for enhancing customer-linking marketing capabilities such as CRM and brand management, but it alone is not sufficient to improve marketing performance. Firms wishing to improve marketing performance should leverage the knowledge and insights gained from MAU to enhance their critical customer-linking marketing capabilities.

This study explicates the capability-developing mechanisms through which a firm can use its market-sensing capability as manifested by MAU to enhance customer-linking marketing capabilities and to improve its marketing performance. In so doing, this study extends our understanding of the critical role of absorptive capacity in helping firms identify, assimilate, transform and apply valuable external knowledge.

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.

Drawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.

The results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.

The conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

This study examines the relationship between knowledge-sharing activities of the firm and its innovation capability. It also investigates the moderating impact of the firms' absorptive capacity on the relationship between knowledge sharing and firm innovation capability from the cross-subsidiary perspective in the international market environment.

This study reviewed the literature from the areas of knowledge management, international market and innovation management. Through the literature review, absorptive capacity theory and dynamic capability view (DCV) theory, a conceptual model has been developed. This model has been validated using partial least squares structural equation modeling (PLS-SEM) technique with 612 respondents from 16 multinational firms from different countries.

The study finds that knowledge-sharing activities across subsidiaries of multinational firms are important for product and process innovation. Firms’ absorptive capacity also impacts the relationship between firms' knowledge-sharing activities and their different dynamic capabilities, such as sensing, seizing and transforming. The study also finds that firms' innovation capability positively impacts their competitiveness.

This study provides valuable inputs to the management of multinational firms to recognize the importance of knowledge-sharing activities across their different subsidiaries in the international marketing knowledge management (MKM) context.

The study adds to the literature on knowledge management, international market and firms' innovation capability. As the study examines the knowledge-sharing activities across different subsidiaries of multinational firms, especially in the context of process and product innovation, it is considered unique. The study also provides a unique validated model.

This paper aims to review and synthesise the recent advancements in the business model literature and explore how firms approach business model innovation.

A systematic review of business model innovation literature was carried out by analysing 219 papers published between 2010 and 2016.

Evidence reviewed suggests that rather than taking either an evolutionary process of continuous revision, adaptation and fine-tuning of the existing business model or a revolutionary process of replacing the existing business model, firms can explore alternative business models through experimentation, open and disruptive innovations. It was also found that changing business models encompasses modifying a single element, altering multiple elements simultaneously, and/or changing the interactions between elements in four areas of innovation: value proposition, operational value, human capital and financial value.

The conflicting approaches exist in the literature on how firms change their business models, this review synthesises these approaches and provide a clear guidance as to the ways through which business model innovation can be undertaken.