scholarly journals Text mining analysis roadmap (TMAR) for service research

2020 ◽  
Vol 34 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Mohamed Zaki ◽  
Janet R. McColl-Kennedy
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Design Methodology ◽  
Data Sources ◽  
Theory Testing ◽  
Service Research ◽  
Data Types ◽  
Content Type ◽  
Research And Practice ◽  
Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

Applying the EEE customer mindset in luxury: reevaluating customer experience research and practice during and after corona

2020 ◽  
Vol 31 (6) ◽  
pp. 1175-1183 ◽  
Author(s):  
Philipp ‘Phil' Klaus ◽  
Aikaterini Manthiou
Keyword(s):  
Design Methodology ◽  
Customer Experience ◽  
Service Research ◽  
Research Directions ◽  
Content Type ◽  
Research And Practice ◽  
Related Service ◽  
Luxury Industry ◽  
The Impact ◽  
Review Current

PurposeThis paper’s objective is to raise awareness of how customer experience (CX) research, a key construct of modern-day service research, needs to be revisited in view of the pandemic. Particularly, we examine whether CX-related service research constructs, models and frameworks need to be reevaluated during and after the Corona crisis and if so, how and why? Moreover, this paper contributes to CX research by analyzing the customer mindset from three perspectives: emotions, employment and expectations (EEE).Design/methodology/approachWe critically review current CX practices and investigate the impact on how customers perceive services in this time of crisis.FindingsBased on this critical analysis, we discuss implications for research and practice with reference to the example of the luxury industry with its historical emphasis on the CX. This discussion leads to related propositions and research directions through Corona and beyond.Originality/valueWe investigate the current customer mindset in more detail, which we divide into three main themes: emotions, employment and expectations (EEE).

Editorial: the changing nature of data

2016 ◽  
Vol 30 (7) ◽  
pp. 673-675 ◽  
Author(s):  
Steve Baron ◽  
Rebekah Russell-Bennett
Keyword(s):  
Design Methodology ◽  
Services Marketing ◽  
Data Sources ◽  
Service Research ◽  
Data Sets ◽  
Data Handling ◽  
Large Problem ◽  
Content Type ◽  
Innovative Methods ◽  
Existing Data

Purpose The purpose of this paper it to highlight the challenges of managing and handling data for services marketers that have been brought about by the contemporary environment and emerging schools of thought. Design/methodology/approach A comparison is made between conventional data collection and statistical analysis, and the need to glean information from large, pre-existing data sets for future contributions to service research. Findings For service marketers to tackle real world, large problem areas, there will be a need to develop methods of dealing with data which pre-exist in many forms, as well as data that are collected via well-established procedures. Originality/value The study should be an encouragement for services marketing researchers to develop innovative methods of data handling which recognize a world of burgeoning data sources and types.

From words to pixels: text and image mining methods for service research

2019 ◽  
Vol 30 (5) ◽  
pp. 593-620 ◽  
Author(s):  
Francisco Villarroel Ordenes ◽  
Shunyuan Zhang
Keyword(s):  
Text Mining ◽  
Service Research ◽  
Frontline Employees ◽  
Image Mining ◽  
Text And Image ◽  
Research Directions ◽  
Content Type ◽  
Customer Reviews ◽  
Marketing Operations ◽  
Mining Methods

Purpose The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research. Design/methodology/approach On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided. Findings The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services. Research limitations/implications This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods. Practical implications The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience. Originality/value The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Blockchain as supply chain technology: considering transparency and security

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pei Xu ◽  
Joonghee Lee ◽  
James R. Barth ◽  
Robert Glenn Richey
Keyword(s):  
Supply Chain ◽  
Text Mining ◽  
Information Security ◽  
Design Methodology ◽  
Use Cases ◽  
Future Research ◽  
Content Type ◽  
Blockchain Technology ◽  
Twitter Data ◽  
The Way

PurposeThis paper discusses how the features of blockchain technology impact supply chain transparency through the lens of the information security triad (confidentiality, integrity and availability). Ultimately, propositions are developed to encourage future research in supply chain applications of blockchain technology.Design/methodology/approachPropositions are developed based on a synthesis of the information security and supply chain transparency literature. Findings from text mining of Twitter data and a discussion of three major blockchain use cases support the development of the propositions.FindingsThe authors note that confidentiality limits supply chain transparency, which causes tension between transparency and security. Integrity and availability promote supply chain transparency. Blockchain features can preserve security and increase transparency at the same time, despite the tension between confidentiality and transparency.Research limitations/implicationsThe research was conducted at a time when most blockchain applications were still in pilot stages. The propositions developed should therefore be revisited as blockchain applications become more widely adopted and mature.Originality/valueThis study is among the first to examine the way blockchain technology eases the tension between supply chain transparency and security. Unlike other studies that have suggested only positive impacts of blockchain technology on transparency, this study demonstrates that blockchain features can influence transparency both positively and negatively.

From service quality to experience – and back again?

2015 ◽  
Vol 7 (1) ◽  
pp. 107-119 ◽  
Author(s):  
Jon Sundbo
Keyword(s):  
Service Quality ◽  
Design Methodology ◽  
Service Management ◽  
Service Marketing ◽  
Service Research ◽  
Experience Economy ◽  
Content Type ◽  
The Past ◽  
Functional Quality ◽  
Original Contribution

Purpose – This paper aims to analyse the movement in the focus on customers within service management and marketing theories and service research that has taken place during the past three decades. The paper addresses the question: How did we, in service research, change from emphasizing quality to emphasizing experience? Design/methodology/approach – The paper analyses developments in service and experience theories. Experience has come onto the theoretical agenda, both in its own right and as a concept within service marketing and management theory. Findings – Experience has increasingly been a concept that has replaced quality in service marketing theories. However, an independent experience economy paradigm has also emerged. Recently, the societal emphasis on productivity may lead back to functional quality re-emerges in theories; however, it will most likely be in a new version. Originality/value – This analysis is a profound theory-critical analysis of the actually very widely used concept experience in service theories. The analysis present an understanding of what experience means in these theories and how it relates to the quality concept. This is an original contribution to a deeper understanding of service marketing and service quality theories.

scholarly journals Organisational and professional challenges amid the evolution of sustainability reporting: a theoretical framework and an agenda for future research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alessandro Lai ◽  
Riccardo Stacchezzini
Keyword(s):  
Design Methodology ◽  
Sustainability Reporting ◽  
Future Research ◽  
Special Issue ◽  
Content Type ◽  
Professional Capital ◽  
Research And Practice ◽  
Corporate Reporting ◽  
Professional Challenges ◽  
Reporting Practice

Purpose This paper aims to trace subsequent steps of the sustainability reporting evolution in terms of changes in the organisation fields and professional jurisdictions involved. As such, it highlights the (interrelated) organisational and professional challenges associated with the progressive incorporation of “sustainability” within corporate reporting. Design/methodology/approach The paper draws on Suddaby and Viale’s (2011) theorisation of how professionals reshape organisational fields to highlight how organisational spaces, actors, rules and professional capital evolve alongside the incorporation of sustainability within corporate reporting. Findings The paper shows organisational spaces, actors, rules and professional capital mobilised during the recent evolution of sustainability reporting, starting from a period in which there was no space for sustainability, to more recent periods in which sustainability gained increasing momentum beyond initial niches, and culminating in more integrated forms of sustainability reporting. Research limitations/implications Although the analysis is limited to empirical evidence collected by prior research and practice on sustainability reporting, the paper offers a view to imagine how the incorporation of sustainability within corporate reporting relies on and affects organisational fields and professional jurisdictions. Originality/value The paper offers a lens to interpret corporate and professional challenges associated with the more recent evolutions of sustainability reporting practice and standard setting. It also allows framing the papers accepted in the special issue on “new challenges in sustainability reporting” and concludes by suggesting an agenda for future research.

Data mining in anti-money laundering field

2017 ◽  
Vol 20 (3) ◽  
pp. 301-310 ◽  
Author(s):  
Noriaki Yasaka
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Money Laundering ◽  
Design Methodology ◽  
Point Of View ◽  
Important Task ◽  
Content Type ◽  
Suspicious Transaction ◽  
Mining Methods ◽  
The Creation

Purpose This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management in the global AML field. Originality/value The report considers the creation with data mining methods and utilization from the point of view of knowledge management.

Theory usage in empirical operations management research: a review and discussion

2016 ◽  
Vol 54 (10) ◽  
pp. 2413-2432 ◽  
Author(s):  
Thomas Kenworthy ◽  
Jaydeep Balakrishnan
Keyword(s):  
Operations Management ◽  
Empirical Data ◽  
Design Methodology ◽  
Theory Testing ◽  
Management Research ◽  
Content Type ◽  
Scientific Fields ◽  
Over Time

Purpose The purpose of this paper is to analyze more than three decades of theory testing published in leading operations management (OM) journals. Design/methodology/approach This piece examines the amount of theory testing, the extent to which theories are tested multiple times, and the disciplinary origins of the theories that are tested. Findings The analysis revealed that empirical OM researchers have increasingly responded to demands for more theory-driven knowledge over time. OM researchers are developing and using a wide array of domestic theories to understand empirical data. The examination also revealed a substantial focus on theory borrowed from other scientific fields. Originality/value The findings here suggest that OM is clearly a maturing discipline. As the discipline matures, it is important to consider to what extent borrowed theories and frameworks can offer value to OM. A preliminary vetting model is advanced in order to critically assess foreign theory. It is hoped that future screening promotes only the most useful non-domestic theory, thereby ensuring sufficient journal space for domestic theory and resulting in effective solutions to the pressing, practical problems of the OM field.

Quantifying human resource management: a literature review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Clotilde Coron
Keyword(s):  
Artificial Intelligence ◽  
Resource Management ◽  
Literature Review ◽  
Human Resource Management ◽  
Human Resource ◽  
Design Methodology ◽  
Theoretical Framework ◽  
Data Sources ◽  
Content Type

PurposeWith a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?Design/methodology/approachThis study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.FindingsThe analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.Originality/valueThis literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

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