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Article
Publication date: 23 November 2022

Wu He, Jui-Long Hung and Lixin Liu

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate…

Abstract

Purpose

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

Findings

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

Details

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

Keywords

Article
Publication date: 13 February 2019

Michael DiClaudio

Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s…

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Abstract

Purpose

Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.

Design/methodology/approach

The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”

Findings

According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.

Research limitations/implications

Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.

Practical implications

Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.

Social implications

There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.

Originality/value

This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.

Details

Strategic HR Review, vol. 18 no. 2
Type: Research Article
ISSN: 1475-4398

Keywords

Article
Publication date: 31 October 2011

Tobias Klatt, Marten Schlaefke and Klaus Moeller

Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study

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Abstract

Purpose

Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study aims to reveal whether or not the application of business analytics in strategic planning contributes to better company performance, and to formulate recommendations on how to integrate business analytics in companies' performance management systems.

Design/methodology/approach

Based on a survey conducted with 89 respondents from high‐technology firms, a group comparison between firms with strong performance and those with weak performance reveals significant differences between the two groups' strategic planning processes and application of business analytics.

Findings

The empirical survey's results show that better‐performing companies are characterized by a more sophisticated analytical planning process. Lower‐performing firms acknowledge this competitive advantage. Based on these findings, the authors develop recommendations on how to integrate business analytics in performance management contexts.

Research limitations

The empirical study's results are limited to high‐technology industries in the cultural setting of Germany.

Practical implications

The empirical results emphasize the competitive advantage gained by applying business analytics. The recommendations concerning analytical performance management should help managers to sensibly integrate the analytical toolbox in performance management contexts.

Originality/value

This paper combines insights on the best usage of business analytics from the perspective of strategic planning experts, with recommendations for the integration of business analytics into the performance management framework from an academic perspective.

Details

Journal of Business Strategy, vol. 32 no. 6
Type: Research Article
ISSN: 0275-6668

Keywords

Article
Publication date: 21 September 2015

Anthony Marshall, Stefan Mueck and Rebecca Shockley

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for…

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Abstract

Purpose

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Design/methodology/approach

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Findings

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Research limitations/implications

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Practical implications

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

Originality/value

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

Keywords

Article
Publication date: 15 November 2022

Hua Liu and Shaobo Wei

Building on the information processing perspective, the authors propose that both internal and external supply chain risk management (SCRM) practices play essential roles…

Abstract

Purpose

Building on the information processing perspective, the authors propose that both internal and external supply chain risk management (SCRM) practices play essential roles in mediating supply chain disruption orientation (SCDO) to exercise an influence on resilience. The authors also put forward analytics capability as an important moderator in the above-mediated relationship.

Design/methodology/approach

The authors collected 170 match-paired questionnaires from Chinese firms to test our model. The authors further interviewed some managers to supplement key quantitative results.

Findings

First, SCDO positively affects resilience via internal and external SCRM practices. Second, the mediating effects of internal and external SCRM practices are stronger when analytics capability is higher. Third, analytics capability positively moderates the positive effect of SCDO on SCRM practices. Meanwhile, it does not moderate the positive effect of SCRM practices on resilience.

Research limitations/implications

Our study contributes to SCRM-related and IT-related literature by considering the content, mediating mechanisms (i.e. internal and external SCRM practices) and boundary conditions (i.e. data analytic capability) of SCDO in shaping resilience in the digital supply chain.

Practical implications

Our study helps remind managers that firms build disruption orientation, develop different SCRM practices and leverage analytics capability to improve resilience amid unexpected and unplanned disruptions.

Originality/value

Our study sheds light on the roles of both internal and external SCRM practices. Furthermore, this research helps explain how SCDO motivates resilience through SCRM practices, particularly for those firms that have higher analytics capability.

Details

International Journal of Physical Distribution & Logistics Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0960-0035

Keywords

Article
Publication date: 8 September 2022

Meenal Arora, Anshika Prakash, Amit Mittal and Swati Singh

This study aimed to evaluate the factors that determine an individual's decision to adopt human resources (HR) analytics. This study attempts to extend Unified Theory of…

Abstract

Purpose

This study aimed to evaluate the factors that determine an individual's decision to adopt human resources (HR) analytics. This study attempts to extend Unified Theory of Acceptance and Use of Technology - 2 (UTAUT2) to identify the lag rate in adoption.

Design/methodology/approach

Responses were obtained from 387 HR employees of the Banking Financial Services and Insurance (BFSI) sector in metropolitan cities of India through nonprobabilistic purposive sampling. The analysis was performed through hierarchical regression, structural equation modeling and moderation of resistance to change.

Findings

The results suggest that performance expectancy, hedonic motivation and data availability are endorsed by proponents of the intention to adopt HR analytics. In contrast, effort expectancy, social influence, quantitative self-efficacy and habits did not influence behavioral intention (BI). Additionally, the actual use behavior (UB) of HR analytics was determined by BI and facilitating conditions. Furthermore, the moderating effect of resistance to change is explored.

Practical implications

This study makes a significant contribution to the literature on the adoption of HR analytics. By appropriately concentrating on the adoption intention of HR analytics, organizations can intensify healthy employee relationships, thus encouraging the actual usage of HR analytics.

Originality/value

This study formulates a conceptual framework for the adoption of HR analytics that can be used by top management to formulate strategies for the implementation of HR analytics. Moreover, this study aimed to expand UTAUT2, emphasizing the concept of data availability and quantitative self-efficacy and examining the moderating role of resistance to change in the relationship between BI and UB.

Details

Evidence-based HRM: a Global Forum for Empirical Scholarship, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-3983

Keywords

Article
Publication date: 7 October 2022

Kiran Dhankhar and Abhishek Singh

Drawing upon the career construction theory, the present study aims to propose and test a mediation model exploring the association among technology readiness, adoption of…

Abstract

Purpose

Drawing upon the career construction theory, the present study aims to propose and test a mediation model exploring the association among technology readiness, adoption of human resource (HR) analytics by HR professionals, and organizational career growth.

Design/methodology/approach

A survey has been conducted to collect data from HR professionals (N = 347) working in various industrial sectors in India. The data collected is analyzed for mediation using SPSS PROCESS Macro (Model 4).

Findings

The study provided evidence about the mediating role of individual adoption of HR analytics between technology readiness (motivators, inhibitors) and organization career growth (career goal progress, professional ability development, promotion speed and remuneration growth).

Research limitations/implications

Firstly, the results provide evidence in relation to the career construction theory with respect to adoption of human resource analytics by HR professionals. Secondly, the study findings validate the technology readiness model in the context of adopting HR analytics. Thirdly and most significantly, the study proposes a novel theoretical framework for adoption of HR analytics by HR professionals in organizations.

Practical implications

The findings imply that HR professionals' technology readiness (motivators and inhibitors) can facilitate better adoption of HR analytics in organizations. Moreover, the adoption of HR analytics shall promote better career growth for HR professionals.

Originality/value

The present study builds and tests a theoretical framework based on technology readiness, individual adoption of HR analytics, and organization career growth. The study is the first of its kind to the best of the authors' knowledge.

Details

Evidence-based HRM: a Global Forum for Empirical Scholarship, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-3983

Keywords

Article
Publication date: 27 September 2022

Bowen Hui

The purpose of this work is to illustrate the processes involved in managing teams in order to assist designers and developers to build software that support teamwork. A…

Abstract

Purpose

The purpose of this work is to illustrate the processes involved in managing teams in order to assist designers and developers to build software that support teamwork. A deeper investigation into the role of team analytics is discussed in this article.

Design/methodology/approach

Many researchers over the past several decades studied the success factors of a team. Despite many efforts, there is still no consensus on how a team should ideally be formed. Consequently, how one decides to form teams in a class depends on the domain, classroom context and pedagogical objectives. Therefore, software used to support an instructor in forming teams must be flexible enough to accommodate a variety of use cases and support the users throughout the lifecycle of teamwork. In this work, the author proposes a framework for designing general-purpose team management software. The author reviews existing team formation software and focuses specifically on opportunities for advancing research in team analytics.

Findings

In this context, the author identifies four areas of research opportunities for team analytics.

Originality/value

Lastly, the author proposes a series of research questions (RQs) and discusses the pedagogical, design, technical and social challenges involved.

Details

The International Journal of Information and Learning Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-4880

Keywords

Article
Publication date: 11 October 2022

Ayman Wael Al-Khatib

This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation…

Abstract

Purpose

This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.

Design/methodology/approach

This study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.

Findings

The results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.

Research limitations/implications

The study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.

Practical implications

The findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.

Originality/value

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.

Details

Business Process Management Journal, vol. 28 no. 5/6
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 12 October 2022

Jonathan Peterson, Loubna Tahssain-Gay, David Salvetat, Fabienne Perez and Sophie Hennekam

This article aims to examine the factors that influence how managers approach data analytics.

Abstract

Purpose

This article aims to examine the factors that influence how managers approach data analytics.

Design/methodology/approach

The authors draw on content analysis of 34 in-depth interviews with managers in various sectors in France.

Findings

Using Resource Orchestration Theory as the theoretical lens, the findings show that an understanding of the importance of data analytics, having the skills to effectively use data analytics and the capability to integrate data analytics throughout organizations impact the approach adopted by managers. Based on these interrelated factors, a typology of four different approaches is identified: buyer-users, segmenters, promoters and implementers.

Research limitations/implications

The authors' study reflects results from multiple industries instead of one particular sector. Delving deeper into the practices of distinct sectors with respect to the authors' typology would be of interest.

Practical implications

The study points to the role of managers and more specifically managers' perception of the opportunities and challenges related to data analytics. These perceptions emerge in managers' skills and capacity to understand and integrate dimensions of data analytics that go beyond one's areas of expertise in order to create capabilities towards an organization's advantage.

Originality/value

The authors contribute by revealing three interrelated factors influencing how managers approach data analytics in managers' organizations. The authors address the need expressed by practitioners to better identify factors responsible for adoption and effective use of data analytics.

Details

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

Keywords

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