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Article
Publication date: 5 August 2021

Najah Almazmomi, Aboobucker Ilmudeen and Alaa A. Qaffas

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's…

Abstract

Purpose

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.

Design/methodology/approach

This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).

Findings

The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.

Originality/value

This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 21 May 2021

Rohit Agrawal, Vishal Ashok Wankhede, Anil Kumar and Sunil Luthra

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify…

Abstract

Purpose

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

Design/methodology/approach

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

Findings

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

Originality/value

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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Article
Publication date: 23 September 2019

Muhammad Irfan and Mingzheng Wang

The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and…

Abstract

Purpose

The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan.

Design/methodology/approach

The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan.

Findings

The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI.

Practical implications

The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services.

Originality/value

The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.

Details

British Food Journal, vol. 121 no. 11
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 25 July 2019

Yinhua Liu, Rui Sun and Sun Jin

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality…

Abstract

Purpose

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

Design/methodology/approach

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

Findings

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

Originality/value

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 15 May 2019

Devon S. Johnson, Laurent Muzellec, Debika Sihi and Debra Zahay

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function…

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1934

Abstract

Purpose

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation.

Design/methodology/approach

A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions.

Findings

The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization.

Originality/value

From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

Details

Journal of Research in Interactive Marketing, vol. 13 no. 2
Type: Research Article
ISSN: 2040-7122

Keywords

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Article
Publication date: 19 May 2021

Mauricius Munhoz de Medeiros and Antônio Carlos Gastaud Maçada

In the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive…

Abstract

Purpose

In the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA).

Design/methodology/approach

A survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests.

Findings

The evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA; BA in the relationship between DDC and CA; and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA.

Practical implications

This study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA.

Originality/value

This study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 19 May 2021

Shahriar Akter, Md Afnan Hossain, Qiang (Steven) Lu and S.M. Riad Shams

Big data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international…

Abstract

Purpose

Big data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.

Design/methodology/approach

The study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.

Findings

The findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.

Research limitations/implications

The study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.

Originality/value

The study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

Details

International Marketing Review, vol. 38 no. 5
Type: Research Article
ISSN: 0265-1335

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Article
Publication date: 6 August 2020

Qasim Ali Nisar, Nadia Nasir, Samia Jamshed, Shumaila Naz, Mubashar Ali and Shahzad Ali

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among…

Abstract

Purpose

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.

Design/methodology/approach

The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.

Findings

Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.

Practical implications

This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.

Originality/value

The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

Details

Journal of Enterprise Information Management, vol. 34 no. 4
Type: Research Article
ISSN: 1741-0398

Keywords

Content available
Article
Publication date: 3 September 2020

Manlio Del Giudice, Roberto Chierici, Alice Mazzucchelli and Fabio Fiano

This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply…

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Abstract

Purpose

This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.

Design/methodology/approach

This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.

Findings

The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.

Originality/value

This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.

Details

The International Journal of Logistics Management, vol. 32 no. 2
Type: Research Article
ISSN: 0957-4093

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Article
Publication date: 21 December 2020

Aalok Kumar and Ramesh Anbanandam

Freight transportation practices accounted for a significant share of environmental degradation and climate change over the years. Therefore, environmentally responsible…

Abstract

Purpose

Freight transportation practices accounted for a significant share of environmental degradation and climate change over the years. Therefore, environmentally responsible transport practices (ERTPs) become a serious concern of freight shippers and transport service providers. Past studies generally ignored the assessment of ERTPs of freight transport companies during a transport service contract. To bridge the above literature gap, this paper proposed a hierarchical framework for evaluating freight transport companies based on ERTPs.

Design/methodology/approach

In a data-driven decision-making environment, transport firm selection is affected by multiple expert inputs, lack of information availability, decision-making ambiguity and background of experts. The evaluation of such decisions requires a multi-criteria decision-making method under a group decision-making approach. This paper used a data-driven method based on the intuitionistic fuzzy-set-based analytic hierarchy process (IF-AHP) and VIseKriterijumska Kompromisno Rangiranje (IF-VIKOR) method. The applicability of the proposed framework is validated with the Indian freight transport industry.

Findings

The result analysis shows that environmental knowledge sharing among freight transport actors, quality of organizations human resource, collaborative green awareness training programs, promoting environmental awareness program for employees and compliance of government transport emission law and practice have been ranked top five ERTPs which significantly contribute to the environmental sustainability of freight transport industry. The proposed framework also ranked freight transport companies based on ERTPs.

Research limitations/implications

This research is expected to provide a reference to develop ERTPs in the emerging economies freight transport industry and contribute to the development of a sustainable freight transport system.

Originality/value

This study assesses the environmental responsibility of the freight transportation industry. The emerging economies logistics planners can use proposed framework for assessing the performance of freight transportation companies based on ERTPs.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

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