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Article
Publication date: 26 January 2021

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

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Article
Publication date: 24 March 2020

Guangming Cao and Na Tian

Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important…

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1223

Abstract

Purpose

Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important drivers of marketing performance and that marketing analytics use (MAU) enables firms to gain valuable knowledge and insights for improving firm performance. However, there has been little focus on how firms improve their CRM and brand management via MAU. This study aims to draw on the absorptive capacity theory, research on marketing capabilities and marketing analytics to examine the capability-developing mechanisms that enable a firm to use marketing analytics to enhance its CRM and brand management capabilities, thereby improving its marketing performance.

Design/methodology/approach

A research model is developed and tested based on an analysis of 289 responses collected using an online survey from middle and senior managers of Chinese firms with sufficient knowledge and experience in using marketing analytics for survey participation.

Findings

The findings demonstrate that MAU is positively related to both CRM and brand management capabilities, which in turn are positively associated with marketing performance; and that both CRM and brand management capabilities mediate the relationship between MAU and marketing performance.

Research limitations/implications

The study’s outcomes were based on data collected from a survey, which was distributed using mass e-mails. Thus, the study is unable to provide a meaningful response rate. The research results are based on and limited to Chinese firms.

Practical implications

MAU is essential for enhancing customer-linking marketing capabilities such as CRM and brand management, but it alone is not sufficient to improve marketing performance. Firms wishing to improve marketing performance should leverage the knowledge and insights gained from MAU to enhance their critical customer-linking marketing capabilities.

Originality/value

This study explicates the capability-developing mechanisms through which a firm can use its market-sensing capability as manifested by MAU to enhance customer-linking marketing capabilities and to improve its marketing performance. In so doing, this study extends our understanding of the critical role of absorptive capacity in helping firms identify, assimilate, transform and apply valuable external knowledge.

Details

Journal of Business & Industrial Marketing, vol. 35 no. 7
Type: Research Article
ISSN: 0885-8624

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Article
Publication date: 4 September 2020

Jing Lu, Lisa Cairns and Lucy Smith

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight…

Abstract

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Details

Journal of Modelling in Management, vol. 16 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 29 November 2018

Yudi Fernando, Ramanathan R.M. Chidambaram and Ika Sari Wahyuni-TD

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

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2686

Abstract

Purpose

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

Design/methodology/approach

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

Findings

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

Practical implications

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Originality/value

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

Details

Benchmarking: An International Journal, vol. 25 no. 9
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 18 September 2019

Samir Yerpude and Tarun Kumar Singhal

The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a…

Abstract

Purpose

The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a classical retail business to customer domain.

Design/methodology/approach

The study presented is twofold. First, it empirically tests a theoretical model where the impact of different parameters influencing customer engagement are validated, and its influence on the resultant parameters, i.e. brand loyalty and brand ambassador, is analyzed. Second, it emphasizes on the use of real-time IoT origin data in customer analytics to determine a customer engagement strategy.

Findings

Results indicate that the four parameters, i.e., value propositions basis the buying patterns, loyalty programs, personalized communication and involving the customer in the new development process are influencing customer engagement positively, whereas the parameter loyalty program scores the maximum regression weight. IoT plays a crucial role in generating the real-time data used for generating customer analytics that proves to be vital for the longevity of the organization.

Practical implications

The organizations need judicious blend of four parameters such as value proposition based on buying patterns, participation in new product development, personalized communication and loyalty program while designing the customer engagement strategy. Results drawn from the focused group interview highlight the power of IoT origin real-time data in the customer analytics further strengthening the need of customer centricity in an organization.

Originality/value

Identified need of building a customer engagement strategy for an emerging market with the help of IoT data is addressed in this paper that is identified as an unexplored area and a research gap.

Details

International Journal of Emerging Markets, vol. 16 no. 1
Type: Research Article
ISSN: 1746-8809

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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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3350

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: 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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2110

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

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Article
Publication date: 17 June 2020

Yichuan Wang, Minhao Zhang, Ying Kei Tse and Hing Kai Chan

Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer

Abstract

Purpose

Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer satisfaction (CS) is contingent on the characteristics of different external stakeholders, including business partners (i.e. partner diversity), competitors (i.e. localised competition) and customers (i.e. customer engagement).

Design/methodology/approach

Using both subjective and objective measures from multiple sources, we collected primary data from 141 hotels operating in Greece and their archival data from TripAdvisor and the Hellenic Chamber of Hotels (HCH) database to test the hypothesised relationships. Data were analysed through structural equation modelling.

Findings

This study confirms the positive association between SMA and CS, but it remains subject to the varied characteristics of external stakeholders. We find that an increase in CS due to the implementation of SMA is more pronounced for firms that (1) adopt a selective distribution strategy where a limited number of business partners are chosen for collaboration or (2) operate in a highly competitive local environment. The results further indicate that high level of customer engagement amplifies the moderating effect of partner diversity (when it is low) and localised competition (when it is high) on the SMA–CS relationship.

Originality/value

The study provides novel insights for managers on the need to consider external stakeholder characteristics when implementing SMA to enhance firms' CS, and for researchers on the value of studying SMA implementation from the CT perspective.

Details

International Journal of Operations & Production Management, vol. 40 no. 5
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 30 September 2021

Javier Lorente-Martínez, Julio Navío-Marco and Beatriz Rodrigo-Moya

The purpose of this study is to analyse the level of adoption of in-store analytics by brick-and-mortar retailers. Web analytics technology has been widely adopted by…

Abstract

Purpose

The purpose of this study is to analyse the level of adoption of in-store analytics by brick-and-mortar retailers. Web analytics technology has been widely adopted by online retailers, and the technology to gather similar information in physical stores is already available. This study explores how such technology is valued and adopted by retailers.

Design/methodology/approach

This study is based on interviews and a focus group of 21 retail executives using a semi-structured interview methodology. An in-store analytics service was defined, along with specific key performance indicators (KPIs) and use cases to structure respondents' feedback.

Findings

Although noteworthy differences have been found in the value of KPIs and use cases by type of business, the main finding is that none of the respondents reached the stage of a brick-and-mortar data-driven company. In-store analytics services are in the early stages of Rogers' (1983) model of diffusion of innovations. Three main reasons are presented: lack of technology knowledge, budget priority and a data culture inside the companies.

Practical implications

The results should encourage scholars to further investigate the drivers accelerating the adoption of these technologies. Practitioners and solution providers should strive for improvement in the simplicity of their solutions.

Originality/value

This study is the first to analyse the level of adoption of in-store analytics from the perspective of retailers.

Details

International Journal of Retail & Distribution Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-0552

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Book part
Publication date: 4 December 2020

Samir Yerpude

A paradigm shift is observed in the last decade where transactional marketing is taken over by relationship marketing. Customer relationship management (CRM) has been an…

Abstract

A paradigm shift is observed in the last decade where transactional marketing is taken over by relationship marketing. Customer relationship management (CRM) has been an integral part of a business strategy in the current era. CRM integrates product sales, product marketing and, most importantly, customer service in a seamless manner to generate value for the organization as well as for its customers in short a win-win situation. Profoundly, CRM needs to be a part of the top management agenda and driven top-down instead of an IT initiative. Industrial revolution 4.0 is characterized by cyber-physical systems. Internet of Things (IoT) is the digital technology for the present and future. IoT primarily aids in gathering real-time data and transmitting the same over the internet to a central repository for consuming the same in business models. Real-time customer data analytics can be performed by customer-centric organizations to enhance CRM.

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