Enhancing Customer Participation for Superior Value Outcomes in Knowledge Intensive Business Services

Siirretty Doriast

How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.©2022 SAGE Publications. The article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference.fi=vertaisarvioitu|en=peerReviewed

Free-to-fee transformation of industrial services

Industrial firms venturing into services is a common phenomenon in B2B markets. However, companies are often unable to monetize many such services, thus incurring high costs of service provision without benefiting from revenue generation in return. To address this critical but little-studied problem, we investigate how industrial firms can transform existing free services into for-fee offerings. Employing a theories-in-use approach, we explore leading global firms via a cross-section of B2B industries, including automotive, maritime, material handling, medical equipment, mining and construction tools, and petrochemicals. Contingent on the empirics, we precisely characterize and define free industrial services. Based on the internal and external challenges that firms face in free-to-fee (F2F) transformations, we develop a typology classifying free services into four distinct categories: Front-runners, Tugs of War, In-house Shackles, and Dead Ends. For each category, we provide empirical illustrations and identify critical actions and activities that firms deploy to successfully implement F2F transformations along the dimensions of structures, processes, people, and rewards. Thus, we offer guidance on how to overcome both external and internal challenges. Our findings demonstrate that F2F transformations of industrial services are not isolated marketing, sales, or pricing activities but require a concerted effort among all organizational functions involved.</p

Understanding the impact of online customers’ shopping experience on online impulsive buying: A study on two leading E-commerce platforms

Research offers some indication that the online customers' shopping experience (OCSE) can be a strong predictor of online impulsive buying behavior, but there is not much empirical support available to form a holistic understanding; whether, and indeed how, the effects of the OCSE on online impulsive buying behavior are affected by customers' attitudinal loyalty and self-control are not well understood areas of research. In this study, we examine how functional and psychological dimensions of the OCSE influence online impulsive buying within e-commerce platforms. We will investigate customers' attitudinal loyalty as a mediator between the OCSE and online impulsive buying behavior, and the customers' self-control as a moderator between customers' attitudinal loyalty and online impulsive buying. To analyze these relationships we will conduct an online survey (n = 1489) with customers of two leading Chinese e-commerce platforms: Jindong and Taobao. The findings from structural equation modeling indicate a positive relationship between the tested dimensions of the OCSE and customers' online impulsive buying. We also find a mediating role of customers' attitudinal loyalty and negative moderation of customers’ self-control. Theoretically, the findings contribute to the literature regarding online impulsive buying and the online customer experience. For managers, the findings stress the importance of ethical management with regard to the online shopping experiences.</p

Ensuring client participation to co-create value in knowledge intensive business services : an empirical study on Finnish software firms

Siirretty Doriast

Customer participation in knowledge intensive business services: Perceived value outcomes from a dyadic perspective

Knowledge intensive business services (KIBS) are considered a cornerstone of contemporary developed economies. Successful production and delivery of these services, and thus their perceived value outcomes, highly depend on customer participation (CP) in the service processes. However, the extant understanding of the perceived value outcomes of CP, which is crucial to the appropriate inducement and integration of organizational resources in service processes, is limited. Through the exploratory investigation of three dyadic cases, each comprising one customer and one service provider organization engaged in a knowledge-based service project, this study addressed this crucial topic. Results indicated four categories of perceived value outcomes emerged through CP: functional, economic, relational, and strategic values. The study provides insights on the evolution of value perceptions over time, the individual value components within each value category, and perceptual similarities and differences between customer and provider organizations. Further, these results indicate that various value outcomes of CP receive divergent levels of attention from personnel in different organizational hierarchies. The paper provides useful and applicable suggestions for managers, especially in the context of technology-based KIBS and solutions.</p

A critical analysis of service ecosystems research: rethinking its premises to move forward

International audiencePurposeThis study aims to examine the development of service ecosystems literature and its four premises as follows: the characterization of service ecosystems as loosely coupled systems, the existence of shared institutional arrangements among actors, the occurrence of resource-integrating interactions among actors and value co-creation as the stated purpose of service ecosystems.Design/methodology/approachWith a systematic literature review, the paper identifies and analyzes 98 articles on service ecosystems. An examination and a cross-check of the central elements of the articles reveal gaps and limitations in the analysis of service ecosystems. These results lead to the formulation of four propositions and suggestions for further research.FindingsThe four premises of service ecosystems are constrained by overly optimistic perceptions that prevent theoretical advancements. These premises overlook possible tight coupling; power asymmetries; divergent interpretations of institutions and institutional arrangements; divergent interpretations of actors’ resource-integrating actions, intentions and abilities; and the co-destruction of value. Four propositions are formulated to address these challenges.Research limitations/implicationsThe shortcomings reflect the systematic literature review, which only covers a specific area of the extant knowledge base, namely, English-language articles published in peer-reviewed international journals.Originality/valueThis study extensively and critically investigates the premises of service ecosystems for the first time, proposing a more holistic, dynamic and realistic understanding of them. In so doing, it paves the way for renewed conceptualizations of service ecosystems

Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda.

As remote work has become more common than ever throughout the COVID-19 pandemic, it has drawn special attention from scholars. However, the outcome has been significantly sporadic and fragmented. In our systematic review, we use artificial intelligence-based machine learning tools to examine the relevant extant literature in terms of its dominant topics, diversity, and dynamics. Our results identify-eight research themes: (1) Effect on employees at a personal level, (2) Effect on employees' careers, (3) Family life and gender roles, (4) Health, well-being, and safety, (5) Labor market dynamics, (6) Economic implications, (7) Remote work management, (8) Organizational remote work strategies. With further content analysis, we structure the sporadic research into three overarching categories. Finally, for each category, we offer a detailed agenda for further research. [Abstract copyright: © 2022 Published by Elsevier Inc.