Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility

Please note that the full text of the AAM must only appear in the IR once the final version of the article has been published in the journal. If you have any questions about Emerald’s repository policy and how to make a ‘closed deposit’, please contact [email protected] purpose of this paper is to examine when and how organizations build big data analytics capability to improve supply chain agility and gain competitive advantage. We grounded our theoretical framework in two perspectives the dynamic capabilities view (DCV) and contingency theory (CT). To test our research hypotheses, we gathered 173 usable-responses using a pre-tested questionnaire. Our results suggest that big data analytics capability has a positive and significant effect on supply chain agility and competitive advantage. Further, our results support our hypothesis that organizational flexibility has a positive and significant moderation effect on the path joining big data analytics capability and supply chain agility. However, contrary to our belief, we found no support for the moderation effect of organizational flexibility on the path joining big data analytics capability and competitive advantage. The study makes some useful contributions to the literature on big data analytics capability, supply chain agility, organizational flexibility and competitive advantage. Moreover, our results may further motivate future scholars to replicate our findings using longitudinal data

Predicting Performance - A Dynamic Capability View

Emerald has removed the embargo period across all journals. The full text of the article may therefore become visible within your IR as soon as the final version has been published in the journal.Production planning and resource allocation are ongoing issues that organisations face on a day-to-day basis. The study addresses these issues by developing a dynamic performance measurement system (DPMS) to effectively re-deploy manufacturing resources, thus enhancing the decision-making process in optimising performance output. The study also explores the development of dynamic capabilities through exploitation of the organisational tacit knowledge. The study was conducted using 6-stage action research for developing DPMS with real-time control of independent variables on the production lines to study the impact. The DPMS was developed using a hybrid approach of discrete event simulation (DES) and system dynamics (SD) by using the historical as well as live data from the action case organisation. Through the development of DPMS and by combining the explicit and tacit knowledge, this study demonstrated an understanding of using cause and effect analysis in manufacturing systems to predict performance. Such a DPMS creates agility in decision making and significantly enhances the decision-making process under uncertainty. The research also explored how the resources can be developed and maintained into dynamic capabilities to sustain competitive advantage. The present study provides a starting-point for further research in other manufacturing organisations to generalise findings. The originality of the DPMS model comes from the approach used to build the cause and effect analysis by exploiting the tacit knowledge and making it dynamic by adding modelling capabilities. Originality also comes from the hybrid approach used in developing the DPMS

Social sustainability in the supply chain: Construct development and measurement validation

Research on social sustainability in developing countries has recently gained importance for both academics and practitioners. Studies in the supply chain management field take either a supplier or a manufacturer perspective that address predominantly corporate social responsibility (CSR) issues referring to the internal stakeholders. Our research integrates the literature on supplier, manufacturer, and customer responsibility and proposes the concept of supply chain social sustainability (SCSS) that refers to addressing social issues within the overall (upstream and downstream) supply chain. Furthermore, we develop and empirically validate scales for measuring SCSS using in-depth interviews and a survey in the Indian manufacturing industry. Our results suggest that SCSS consists of six underlying dimensions, namely equity, safety, health and welfare, philanthropy, ethics, human rights, in a 20-item valid and reliable scale. We discuss the implications of the findings for research and practice and suggest future research avenues

Skills needed in supply chain - human agency and social capital analysis in third party logistics

Purpose: A shortage of skills is recognized as a major source of risk in supply chain networks. This study uses two independent organizational theories to explain how to build applicable skills for continuous availability of appropriate supply chain talents. The paper proposes an integrated framework that links human agency theory, social capital theory and supply chain skill. Design/methodology/approach: This framework is analyzed in Third Party Logistics (3PL) organizations by confirmatory factor analysis and tested using a survey. After pre-testing by six academics and six practitioners, and following the total design method, the data were collected from 183 3PL organizations in India. Data was checked to ensure no non-response bias. Research hypotheses were tested using WarpPLS-Structural Equation Modeling. Findings: Primary finding offers guidance to 3PL managers. Their driving role and mediating role of access to information and access to resources facilitate building supply chain skill. Leaders who invest in library, acquiring e-resources, offer financial support and create trust among employees are enablers of building supply chain skill. Research limitations/implications: Practical implications: Originality/value: This study classified fourteen supply chain skill into three categories as: managerial skill, quantitative skill and supply chain core skill. The study could be extended to similar companies in other developing countries

Agility and Resilience as antecedents of Supply Chain Performance under moderating effects of Organizational Culture within Humanitarian Setting: A Dynamic Capability View

Full text embargoed until 18.12.2029 (publisher's embargo period, 12 months

Understanding employee turnover in humanitarian organizations

24 month

Enablers of Six Sigma: contextual framework and its empirical validation

The aim of the paper is to identify the enablers for the successful implementation of Six Sigma. None of the existing frameworks provides any clear understanding related to linkages between, and hierarchical relationships among, the constructs of Six Sigma implementation. Our study has both inductive and deductive elements. We identified enablers of Six Sigma implementation from existing research, and we developed a contextual framework using the interpretive structural modelling technique. We further studied enablers based on their driving power and dependence using MICMAC analysis to categorise the enablers into four clusters. In order to validate the ISM model statistically we developed and pre-tested a structured questionnaire before using it for a survey. Data were collected using a split survey method using a modified version of Dillman\u27s total design method. We performed non-response bias before checking assumptions such as constant variance and normality. We further checked the reliability and construct validity using confirmatory factor analysis. We find that constructs and indicators of our theoretical framework meet the criteria, and find them to be a good fit based on confirmatory factor analysis. We draw conclusions based on statistical analyses and our study limitations, and suggest further research directions

Modelling quality dynamics on business value and firm performance in big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance. The study identifies that system quality (i.e., system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e., completeness, accuracy, format and currency) are key to enhance business value and firm performance in a big data environment. The study also proposes that the relationship between quality and firm performance is mediated by business value of big data. Drawing on the resource based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and firm performance

Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature

Purpose The emergent field of Internet of Things (IoT) has been evolving rapidly with a geometric growth in the number of academic publications in this field. The purpose of this paper is to review the literature of IoT in past 16 years using rigorous bibliometric and network analysis tools, offering at the same time future directions for the IoT research community and implications for managers and decision makers. Design/methodology/approach The authors adopted the techniques of bibliometric and network analysis. The paper reviewed the articles published on IoT from 2000 to 2015. Findings This study identifies top contributing authors; key research topics related to the field; the most influential works based on citations and PageRank; and established and emerging research clusters. Scholars are encouraged to further explore this topic. Research limitations/implications This study focusses only on vision and applications of IoT. Scholars may explore various other aspects of this area of research. Originality/value To the best of authors’ knowledge, this is the first study to review the literature on IoT by using bibliometric and network analysis techniques. The study is unique as it spans a long time period of 16 years (2000-2015). The study proposes a five-cluster classification of research themes that may inform current and future research in IoT