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Article

Morteza Namvar, Ali Intezari and Ghiyoung Im

Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies…

Abstract

Purpose

Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

Design/methodology/approach

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

Findings

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

Research limitations/implications

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

Practical implications

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

Originality/value

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

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Article

Guangming Cao, Yanqing Duan and Na Tian

While marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of…

Abstract

Purpose

While marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs).

Design/methodology/approach

This research employs a fuzzy-set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs.

Findings

The key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use, and (2) the configurations for small firms are different from those for medium-sized firms.

Research limitations/implications

The research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers.

Practical implications

The study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.

Originality/value

The study contributes to the literature by addressing an important yet underresearched area, i.e. marketing analytics use in SMEs, applying a configurational approach to the research phenomenon. It highlights different pathways to marketing analytics use in SMEs. The findings provide empirical evidence on the possibility and implication of marketing analytics use being asymmetrical and different between small and medium-sized firms.

Details

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

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Article

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…

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

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Article

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

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

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Article

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…

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

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Article

Fred Niederman

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution…

Abstract

Purpose

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.

Design/methodology/approach

This paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.

Findings

Neither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.

Research limitations/implications

This essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.

Practical implications

Project managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.

Originality/value

This essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

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Article

Rawan Enad Al-Qaralleh and Tarik Atan

The emergence of the knowledge economy and Industry 4.0 has prompted many firms to invest considerable resources into knowledge-based human resource management (HRM…

Abstract

Purpose

The emergence of the knowledge economy and Industry 4.0 has prompted many firms to invest considerable resources into knowledge-based human resource management (HRM) practices and business analytics capabilities aimed at enhancing competitiveness. This paper aims to propose a conceptual model that examines the collective effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance using knowledge-based theory as a theoretical framework.

Design/methodology/approach

The present study empirically tested the above-said idea by surveying (n = 182) individuals with supervisory capacity in Jordanian 4- and 5-star hotels. The obtained data was analyzed using linear modeling and fuzzy sets (fsQCA) techniques.

Findings

Results from linear modeling revealed that knowledge-based HRM practices, business analytics and organizational agility are important antecedents for innovative performance. Conversely, findings from fsQCA revealed that organizational agility is a necessary and sufficient condition to achieve high innovative performance. While business analytics is a sufficient condition to achieve high innovative performance.

Originality/value

This study is among the first to unveil the linear and complimentary effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance. Implications for theory and practice are discussed.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article

Jukka Hallikas, Mika Immonen and Saara Brax

This study aims to investigate digitalization as a performance driver in supply chains, especially the role of data analytics in the digitalization of procurement. The…

Abstract

Purpose

This study aims to investigate digitalization as a performance driver in supply chains, especially the role of data analytics in the digitalization of procurement. The study investigates how digital procurement capabilities are linked to data analytics capabilities and supply chain operational performance and how this links to business success.

Design/methodology/approach

Using operational and dynamic capabilities as foundations for data analytics capabilities, this paper studied the digital procurement capabilities and proposed the conceptual model and hypotheses for empirical testing. The collected industry survey data and structural equation method are then applied to test the hypotheses.

Findings

The study confirms positive and significant relationships among digital procurement capabilities, data analytics capabilities and supply chain performance. Digital procurement capabilities mediate the positive relationship between external data analytics capabilities and supply chain performance.

Research limitations/implications

This study has some limitations that should be addressed. The empirical study was based on survey data from a questionnaire that was probably challenging for some respondent companies with low levels of digital procurement and data analytics. Also, it is necessary to adopt secondary data to measure business performance in future studies which reduces the effect of subjective bias.

Practical implications

From the managerial point of view, the findings highlight the importance of gaining knowledge from gathered data and digitalized processes. Managers must focus on data utilization capabilities to improve the operational performance expected from the digitalization of supply chain activities. In addition, managers need to consider exploiting of data through new creative approaches as part of standardized operations.

Originality/value

The present study contributes to existing knowledge by investigating the mediating role of data analytics capabilities between the digitalization of procurement and supply chain performance. The findings support a positive relationship between the data analytics capabilities and supply chain performance in digital upstream supply chain procurement processes. The present study also clarifies the impact and role of data analytics capabilities in digital supply chain development and success.

Details

Supply Chain Management: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1359-8546

Keywords

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Article

Yusra Qamar and Taab Ahmad Samad

This paper aims to identify the current research trends and set the future research agenda in the area of human resource (HR) analytics by an extensive review of the…

Abstract

Purpose

This paper aims to identify the current research trends and set the future research agenda in the area of human resource (HR) analytics by an extensive review of the existing literature. The paper aims to capture state of the art and develop an exhaustive understanding of the theoretical foundations, concepts and recent developments in the area.

Design/methodology/approach

A portfolio of 125 articles collected from the Scopus database was systematically analyzed using a two-tier method. First, the evolution, current state of the literature and research clusters are identified using bibliometric techniques. Finally, using content analysis, the research clusters are studied to develop the future research agenda.

Findings

Based on the bibliometric analysis, network analysis and content analysis techniques, this study provides a comprehensive review of the existing literature. The study also highlights future research themes by identifying knowledge gaps based on content analysis of research clusters.

Research limitations/implications

The evolution and the current state of the HR analytics literature are presented. Some specific research questions are also provided to help future research.

Originality/value

This study enriches the literature of HR analytics by integrating bibliometric analysis and content analysis to develop a more systematic and exhaustive understanding of the research area. The findings of this study may assist fellow researchers in furthering their research in the identified research clusters.

Details

Personnel Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0048-3486

Keywords

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Article

Marwa Rabe Mohamed Elkmash, Magdy Gamal Abdel-Kader and Bassant Badr El Din

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To…

Abstract

Purpose

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study.

Design/methodology/approach

Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data.

Findings

The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E).

Research limitations/implications

This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses.

Practical implications

This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies.

Originality/value

This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Details

Accounting Research Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1030-9616

Keywords

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