Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management

2016 ◽  
Vol 20 (1) ◽  
pp. 12-28 ◽  
Author(s):  
Son K. Lam ◽  
Stefan Sleep ◽  
Thorsten Hennig-Thurau ◽  
Shrihari Sridhar ◽  
Alok R. Saboo
Keyword(s):  
Big Data ◽  
Service Providers ◽  
Modern Technology ◽  
Future Research ◽  
Small Data ◽  
Frontline Employees ◽  
Firm Level ◽  
Customer Interaction ◽  
Data Volume ◽  
Improve Service Quality

The advent of new forms of data, modern technology, and advanced data analytics offer service providers both opportunities and risks. This article builds on the phenomenon of big data and offers an integrative conceptual framework that captures not only the benefits but also the costs of big data for managing the frontline employee (FLE)-customer interaction. Along the positive path, the framework explains how the “3Vs” of big data (volume, velocity, and variety) have the potential to improve service quality and reduce service costs by influencing big data value and organizational change at the firm and FLE levels. However, the 3Vs of big data also increase big data veracity, which casts doubt about the value of big data. The authors further propose that because of heterogeneity in big data absorptive capacities at the firm level, the costs of adopting big data in FLE management may outweigh the benefits. Finally, while FLEs can benefit from big data, extracting knowledge from such data does not discount knowledge derived from FLEs’ small data. Rather, combining and integrating the firm’s big data with FLEs’ small data are crucial to absorbing and applying big data knowledge. An agenda for future research concludes.

Big Data Quality for Data Mining in Business Intelligence Applications

2021 ◽  
pp. 64-91
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Quality ◽  
Business Intelligence ◽  
Future Research ◽  
Quality Factors ◽  
Data Volume ◽  
Mining Algorithms ◽  
Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

scholarly journals Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research

2019 ◽  
pp. 089443931988845 ◽  
Author(s):  
Alexander Christ ◽  
Marcus Penthin ◽  
Stephan Kröner
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Predictive Modeling ◽  
Hot Spots ◽  
Quantitative Research ◽  
Hot Spot ◽  
Future Research ◽  
Original Research ◽  
Cultural Education ◽  
Data Volume

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.

Integrating the Split/Analyze/Meta-Analyze (SAM) Approach and a Multilevel Framework to Advance Big Data Research in Psychology

2018 ◽  
Vol 226 (4) ◽  
pp. 274-283 ◽  
Author(s):  
Yucheng Eason Zhang ◽  
Siqi Liu ◽  
Shan Xu ◽  
Miles M. Yang ◽  
Jian Zhang
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Analytical Techniques ◽  
Future Research ◽  
Firm Level ◽  
Research Directions ◽  
Research Issues ◽  
Country Level ◽  
Multilevel Research ◽  
Future Research Directions

Abstract. Though big data research has undergone dramatic developments in recent decades, it has mainly been applied in disciplines such as computer science and business. Psychology research that applies big data to examine research issues in psychology is largely lacking. One of the major challenges regarding the use of big data in psychology is that many researchers in the field may not have sufficient knowledge of big data analytical techniques that are rooted in computer science. This paper integrates the split/analyze/meta-analyze (SAM) approach and a multilevel framework to illustrate how to use the SAM approach to address multilevel research questions with big data. Specifically, we first introduce the SAM approach and then illustrate how to implement this to integrate two big datasets at the firm level and country level. Finally, we discuss theoretical and practical implications, proposing future research directions for psychology scholars.

2. Why is big data special?

2017 ◽  
pp. 14-25
Author(s):  
Dawn E. Holmes
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Statistical Analysis ◽  
Rapid Growth ◽  
Credit Card ◽  
Fraud Detection ◽  
Small Data ◽  
Computing Power ◽  
Data Volume ◽  
And Storage

The rapid growth in computing power and storage has led to progressively more data being collected. Big datasets are certainly large and complex, but in order to fully define ‘big data’ we need first to understand ‘small data’ and its role in statistical analysis. ‘Why is big data special?’ considers the four main characteristics of big data: volume, variety, velocity, and veracity, which present a considerable challenge in data management. The advantages we expect to gain from meeting this challenge and the questions we hope to answer with big data can be understood through data mining. The use of big data mining in credit card fraud detection is discussed.

scholarly journals Does big data analytics influence frontline employees in services marketing?

2017 ◽  
Vol 23 (3) ◽  
pp. 623-644 ◽  
Author(s):  
Saradhi Motamarri ◽  
Shahriar Akter ◽  
Venkat Yanamandram
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Service Providers ◽  
Research Question ◽  
Big Data Analytics ◽  
Frontline Employees ◽  
Skill Levels ◽  
Content Type ◽  
Knowledge Intensive ◽  
The Impact

Purpose Big data analytics (BDA) helps service providers with customer insights and competitive information. It also empowers customers with insights about the relative merits of competing services. The purpose of this paper is to address the research question, “How does big data analytics enable frontline employees (FLEs) in effective service delivery?” Design/methodology/approach The research develops schemas to visualise service contexts that potentially benefit from BDA, based on the literature drawn from BDA and FLEs streams. Findings The business drivers for BDA and its level of maturity vary across firms. The primary thrust for BDA is to gain customer insights, resource optimisation and efficient operations. Innovative FLEs operating in knowledge intensive and customisable settings may realise greater value co-creation. Practical implications There exists a considerable knowledge gap in enabling the FLEs with BDA tools. Managers need to train, orient and empower FLEs to collaborate and create value with customer interactions. Service-dominant logic posits that skill asymmetry is the reason for service. So, providers need to enhance skill levels of FLEs continually. Providers also need to focus on market sensing and customer linking abilities of FLEs. Social implications Both firms and customers need to be aware of privacy and ethical concerns associated with BDA. Originality/value Knitting the BDA and FLEs research streams, the paper analyses the impact of BDA on service. The research by developing service typology portrays its interplay with the typologies of FLEs and BDA. The framework portrays the service contexts in which BD has major impact. Looking further into the future, the discussion raises prominent questions for the discipline.

scholarly journals Value of big data to finance: observations on an internet credit Service Company in China

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Shaofeng Zhang ◽  
Wei Xiong ◽  
Wancheng Ni ◽  
Xin Li
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Credit Rating ◽  
Large Data ◽  
Future Research ◽  
Business Practices ◽  
Data Volume ◽  
Credit Risk Analysis ◽  
Service Businesses

Abstract Background his paper presents a case study on 100Credit, an Internet credit service provider in China. 100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business. The company makes use of Big Data on multiple aspects of individuals’ online activities to infer their potential credit risk. Methods Based on 100Credit’s business practices, this paper summarizes four aspects related to the value of Big Data in Internet credit services. Results 1) value from large data volume that provides access to more borrowers; 2) value from prediction correctness in reducing lenders’ operational cost; 3) value from the variety of services catering to different needs of lenders; and 4) value from information protection to sustain credit service businesses. Conclusion The paper also discusses the opportunities and challenges of Big Data-based credit risk analysis, which needs to be improved in future research and practice.

Big Data for Digital Government

2015 ◽  
pp. 1394-1407 ◽  
Author(s):  
Yu-Che Chen ◽  
Tsui-Chuan Hsieh
Keyword(s):  
Big Data ◽  
Data Management ◽  
Governance Structure ◽  
Future Research ◽  
Business Analytics ◽  
Privacy And Security ◽  
Critical Issues ◽  
Data Volume ◽  
And Performance ◽  
Big Service

“Big data” is one of the emerging and critical issues facing government in the digital age. This study first delineates the defining features of big data (volume, velocity, and variety) and proposes a big data typology that is suitable for the public sector. This study then examines the opportunities of big data in generating business analytics to promote better utilization of information and communication technology (ICT) resources and improved personalization of e-government services. Moreover, it discusses the big data management challenges in building appropriate governance structure, integrating diverse data sources, managing digital privacy and security risks, and acquiring big data talent and tools. An effective big data management strategy to address these challenges should develop a stakeholder-focused and performance-oriented governance structure and build capacity for data management and business analytics as well as leverage and prioritize big data assets for performance. In addition, this study illustrates the opportunities, challenges, and strategy for big service data in government with the E-housekeeper program in Taiwan. This brief case study offers insight into the implementation of big data for improving government information and services. This article concludes with the main findings and topics of future research in big data for public administration.

scholarly journals Management Overconfidence and CSR Activities in Korea with a Big Data Approach

2020 ◽  
Vol 12 (11) ◽  
pp. 4406
Author(s):  
Sun-A Kang ◽  
Sang-Min Cho
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Stock Exchange ◽  
Least Square ◽  
Future Research ◽  
Firm Level ◽  
Ordinary Least Square ◽  
Prior Literature ◽  
Corporate Social ◽  
The Impact

We examined the relationship between management characteristics and corporate social responsibility (CSR) and this relationship was differentiated by the level of corporate governance. Our analysis was undertaken in firms listed on the Korean Stock Exchange (KSE) from 2006 to 2015. We employed Ordinary Least Square (OLS) regression after clustering the standard errors at the firm level in order to examine these relationships. The KEJI (Korea Economic Justice Institute) index was used as a proxy for CSR and a big data-based proxy estimated from multimedia was used as the level of advertising. We showed that there is a positive relationship between overconfident management and CSR activities. We then categorized the CSR activities as primary and social activities and found that overconfident management is more aggressive in primary CSR activities. In addition, overconfident management makes fewer CSR expenditures when the management is in a chaebol firm but promotes more CSR advertisement. This finding indicates that chaebol affiliation controls overinvestment in CSR activities but promotes CSR advertisements by overconfident managers. Similarly, we found consistent results with overconfident owner-managers. Prior literature on CSR activities focuses on the impact of CSR activities on firm performance. In this paper, we elucidated the determinants of CSR activities, so that this research contributes to firms’ decision-making about sustainable management. Our estimation of CSR variables with big data approaches will also guide future research on this issue. We expect our study to be used as a reference for decision-making by relevant authorities and stakeholders.

scholarly journals Managing customer-to-customer interaction (CCI) – insights from the frontline

2019 ◽  
Vol 33 (7) ◽  
pp. 798-814 ◽  
Author(s):  
Richard Nicholls ◽  
Marwa Gad Mohsen
Keyword(s):  
Human Resource Development ◽  
Service Management ◽  
Future Research ◽  
Frontline Employees ◽  
Content Type ◽  
Customer Interaction ◽  
Service Organisation ◽  
Wide Range ◽  
Service Environments ◽  
The Uk

Purpose The purpose of this study is to explore the capacity of frontline employees (FLEs) to provide insights into customer-to-customer interaction (CCI) and its management in service organisations. Design/methodology/approach This exploratory study used focus groups and semi-structured in-depth interviews with FLEs to investigate their experiences and reflections in dealing with CCI in a complex service setting in the UK. Findings FLEs are able to recall CCI encounters, both positive (PCCI) and negative (NCCI), with ease. They are capable of conceptualising and exploring complex nuances surrounding CCI encounters. FLEs can distinguish levels of seriousness of negative CCI and variations in customer sensitivity to CCI. FLEs vary in their comfort in intervening in negative CCI situations. Whilst FLEs draw on skills imparted in an employee-customer interaction context, they would benefit from CCI-specific training. Propositions are advanced for further empirical testing. Research limitations/implications The authors studied FLE views on CCI in a customer-centric service organisation in the UK. Future research should further address the FLE perspective on CCI in less service-driven organisations and in other countries. A wide range of themes for further research are proposed. Practical implications The insights presented will assist service managers to assess the CCI context of their own organisation and develop strategies and guidelines to support FLEs in detecting, understanding and responding to CCI encounters. Social implications The paper highlights and discusses the complexity of intervening in negative CCI encounters in socially inclusive service environments. Originality/value Based on FLE-derived perceptions of CCI, the paper contributes conceptually to CCI knowledge by identifying the existence of “concealed CCI”, distinguishing between gradual and sudden CCI intervention contexts and exploring the human resource development consequences of this distinction, with original implications for service management. The study also contributes to extending the scope of research into triadic service interactions.

scholarly journals Cyborgs as frontline service employees: a research agenda

2019 ◽  
Vol 29 (4) ◽  
pp. 415-437 ◽  
Author(s):  
Tony Garry ◽  
Tracy Harwood
Keyword(s):  
Research Agenda ◽  
Service Providers ◽  
The Body ◽  
Future Research ◽  
Frontline Employees ◽  
Content Type ◽  
Emergent Technologies ◽  
Future Research Agenda ◽  
The Individual ◽  
Service Interactions

Purpose The purpose of this paper is to identify and explore potential applications of cyborgian technologies within service contexts and how service providers may leverage the integration of cyborgian service actors into their service proposition. In doing so, the paper proposes a new category of “melded” frontline employees (FLEs), where advanced technologies become embodied within human actors. The paper presents potential opportunities and challenges that may arise through cyborg technological advancements and proposes a future research agenda related to these. Design/methodology/approach This study draws on literature in the fields of services management, artificial intelligence, robotics, intelligence augmentation (IA) and human intelligence to conceptualise potential cyborgian applications. Findings The paper examines how cyborg bio- and psychophysical characteristics may significantly differentiate the nature of service interactions from traditional “unenhanced” service interactions. In doing so, the authors propose “melding” as a conceptual category of technological impact on FLEs. This category reflects the embodiment of emergent technologies not previously captured within existing literature on cyborgs. The authors examine how traditional roles of FLEs will be potentially impacted by the integration of emergent cyborg technologies, such as neural interfaces and implants, into service contexts before outlining future research directions related to these, specifically highlighting the range of ethical considerations. Originality/value Service interactions with cyborg FLEs represent a new context for examining the potential impact of cyborgs. This paper explores how technological advancements will alter the individual capacities of humans to enable such employees to intuitively and empathetically create solutions to complex service challenges. In doing so, the authors augment the extant literature on cyborgs, such as the body hacking movement. The paper also outlines a research agenda to address the potential consequences of cyborgian integration.

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