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Article
Publication date: 23 November 2021

Mara Soncin and Marta Cannistrà

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational…

Abstract

Purpose

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

Design/methodology/approach

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

Findings

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

Originality/value

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

Details

Qualitative Research in Accounting & Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1176-6093

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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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3182

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
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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7940

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

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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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9982

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

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Article
Publication date: 23 October 2021

Surajit Bag and Muhammad Sabbir Rahman

A circular economy is a popular approach considered by many firms to address sustainable development goals strategically. Literature indicates that collaborative…

Abstract

Purpose

A circular economy is a popular approach considered by many firms to address sustainable development goals strategically. Literature indicates that collaborative relationships among supply chain partners facilitate circular economy practices. However, there is a dearth of studies in lower-middle-income countries indicating the unique challenges industries face whilst practising circular economy principles and how the challenges can be overcome. To address the calls of previous researchers, this study aims to explore the following relationships: engagement and alliance capability whilst data analytics capability plays a mediating role; the relationship between alliance and data analytics capability with sustainable supply chain flexibility whilst industry dynamism is considered as a moderating variable and the relationship between sustainable supply chain flexibility and circular economy-target performance.

Design/methodology/approach

A cross-sectional survey was performed and data was collected from 760 employees of Indian firms. Covariance-based structural equation modelling was applied to perform the path analysis to determine a firm’s capabilities in shaping sustainable supply chain flexibility and enhancing circular economy target performance.

Findings

Drawing upon dynamic capability theory, it was first established that engagement capability has a positive and significant influence on alliance capability, whilst data analytics capability played a partial mediating role. Second, it was established that alliance capability and data analytics capability significantly affect sustainable supply chain flexibility, whilst industry dynamism played a moderating role. Finally, it was clear that sustainable supply chain flexibility had a significant and positive effect on circular economy target performance, ultimately enhancing sustainability.

Originality/value

This study advances the circular economy literature by recommending that firms must consider some critical operational level capabilities to develop their dynamic capability, i.e. sustainable supply chain flexibility, to better meet the competitive market conditions in turbulent business environments.

Details

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

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Article
Publication date: 12 November 2021

Marcello Mariani and Rodolfo Baggio

The purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas…

Abstract

Purpose

The purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas, research streams and gaps and to develop an agenda for future research.

Design/methodology/approach

This research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling.

Findings

The number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as BD analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques.

Originality/value

This work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on BD and analytics, combining these two interconnected topics.

Details

International Journal of Contemporary Hospitality Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 11 October 2021

Siddharth Gaurav Majhi, Arindam Mukherjee and Ambuj Anand

Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these…

Abstract

Purpose

Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization.

Design/methodology/approach

This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization.

Findings

Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring).

Research limitations/implications

This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring.

Originality/value

To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.

Details

VINE Journal of Information and Knowledge Management Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5891

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Article
Publication date: 13 October 2021

Irem Önder and Adiyukh Berbekova

The purpose of this study is to understand the status quo of the use of Web analytics tools by European destination management organizations (DMOs) and to provide…

Abstract

Purpose

The purpose of this study is to understand the status quo of the use of Web analytics tools by European destination management organizations (DMOs) and to provide guidelines in using these metrics for business intelligence and tourism design. In addition, the goal is to improve destination management at the city level using Web analytics data.

Design/methodology/approach

In this exploratory study, the authors analyze how European DMOs view Web analytics data through the lens of the “data to knowledge to results” framework. The authors analyze the use of Web analytics tools by DMOs through the theory of affordances and “data-to-knowledge framework” developed by Davenport et al., which incorporates several factors that contribute to a successful transformation of data available to an organization to knowledge, desirable results and ultimately to building an analytical capability.

Findings

The results show that European DMOs mainly use Web analytics data for website quality assurance, but that some are also using them to drive marketing programs. The study concludes by providing several suggestions for ways in which DMOs might optimize the use of Web analytics data, which will also improve the management of destinations.

Originality/value

Web analytics tools are used by many organizations such as DMOs to collect traffic data, to evaluate and optimize websites. However, these metrics can also be combined with other data such as bednights numbers and used for forecasting or other managerial decisions for destination management at the city level. There is a research gap in this area that focuses on using Web analytics data for business intelligence in the tourism industry and this research aims to fill this gap.

Details

International Journal of Tourism Cities, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-5607

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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

Keywords

Content available
Article
Publication date: 3 September 2021

Mikael Öhman, Ala Arvidsson, Patrik Jonsson and Riikka Kaipia

The purpose of this study is to elaborate on how analytics capability develops within the PSM function. This study is an in-depth exploration of how analytics capability…

Abstract

Purpose

The purpose of this study is to elaborate on how analytics capability develops within the PSM function. This study is an in-depth exploration of how analytics capability develops within the purchasing and supply management (PSM) function.

Design/methodology/approach

A multiple case study was conducted of the PSM function of six case firms, in which primary data were collected through semi-structured interviews with PSM analytics stakeholders. The data were analyzed based on an analytics capability framework derived from the literature. Cases were chosen based on them having advanced PSM practices and ongoing analytics projects in the PSM area.

Findings

The findings shed light on how the firms develop their analytics capability in the PSM functional area. While we identify several commonalities in this respect, the authors also observe differences in how firms organize for analytics, bringing analytics and PSM decision-makers together. Building on the knowledge-based view of the firm, The authors offer a theoretical explanation of our observations, highlighting the user-driven side of analytics development, which has largely been unrecognized by prior literature. The authors also offer an explanation of the observed dual role that analytics takes in cross-functional initiatives.

Research limitations/implications

The exploratory nature of our study limits the generalizability of our results. Further, our limited number of cases and interviewees indicate that there is still much to explore in the phenomenon of developing analytics capability.

Practical implications

Our findings can help firms gain a better understanding of how they could develop their analytics capability and what issues they need to consider when seeking leveraging data through analytics for PSM decisions.

Originality/value

This paper is, to the best knowledge of the authors, the first empirical study of analytics capability in PSM.

Details

International Journal of Physical Distribution & Logistics Management, vol. 51 no. 9
Type: Research Article
ISSN: 0960-0035

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