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Article
Publication date: 16 February 2022

Sateesh Shet and Binesh Nair

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources…

Abstract

Purpose

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

Design/methodology/approach

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

Findings

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Research limitations/implications

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

Originality/value

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.

Article
Publication date: 12 July 2023

Xiaoyan Jiang, Jie Lin, Chao Wang and Lixin Zhou

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated…

Abstract

Purpose

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

Design/methodology/approach

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

Findings

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

Practical implications

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

Originality/value

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 16 March 2023

Ali Ghorbanian and Hamideh Razavi

The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common…

Abstract

Purpose

The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.

Design/methodology/approach

First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.

Finding

According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.

Originality/value

This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

Details

Data Technologies and Applications, vol. 57 no. 5
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 11 August 2023

Drew Woodhouse and Andrew Johnston

Critiques of international business (IB) have long pointed to the weaknesses in the understanding of context. This has ignited debate on the understanding of institutions and how…

Abstract

Purpose

Critiques of international business (IB) have long pointed to the weaknesses in the understanding of context. This has ignited debate on the understanding of institutions and how they “matter” for IB. Yet how institutions matter ultimately depends on how IB applies institutional theory. It is argued that institutional-based research is dominated by a narrow set of approaches, largely overlooking institutional perspectives that account for institutional diversity. This paper aims to forward the argument that IB research should lend greater attention to comparing the topography of institutional configurations by bringing political economy “back in” to the IB domain.

Design/methodology/approach

Using principal components analysis and hierarchical cluster analysis, the authors provide IB with a taxonomy of capitalist institutional diversity which defines the landscape of political economies.

Findings

The authors show institutional diversity is characterised by a range of capitalist clusters and configuration arrangements, identifying four clusters with distinct modes of capitalism as well as specifying intra-cluster differences to propose nine varieties of capitalism. This paper allows IB scholars to lend closer attention to the institutional context within which firms operate. If the configurations of institutions “matter” for IB scholarship, then clearly, a quantitative blueprint to assess institutional diversity remains central to the momentum of such “institutional turn.”

Originality/value

This paper provides a comprehensive survey of institutional theory, serving as a valuable resource for the application of context within international business. Further, our taxonomy allows international business scholars to utilise a robust framework to examine the diverse institutional context within which firms operate, whilst extending to support the analysis of broader socioeconomic outcomes. This taxonomy therefore allows international business scholars to utilise a robust framework to examine the institutional context within which firms operate.

Details

Critical Perspectives on International Business, vol. 19 no. 5
Type: Research Article
ISSN: 1742-2043

Keywords

Article
Publication date: 19 April 2022

Prosenjit Ghosh and Sabyasachi Mukherjee

The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to…

580

Abstract

Purpose

The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.

Design/methodology/approach

Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.

Findings

A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.

Practical implications

Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.

Originality/value

The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.

Details

Journal of Hospitality and Tourism Insights, vol. 6 no. 2
Type: Research Article
ISSN: 2514-9792

Keywords

Article
Publication date: 7 February 2024

Khatab Alqararah and Ibrahim Alnafrah

This research paper aims to contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking groups and exploring learning opportunities and…

Abstract

Purpose

This research paper aims to contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking groups and exploring learning opportunities and integration directions.

Design/methodology/approach

The study employs a multi-dimensional innovation-driven clustering methodology to analyze data from the 2019 edition of the Global Innovation Index (GII). Hierarchical and K-means Cluster Analysis techniques are applied using various sets of distance matrices to uncover and analyze distinct innovation patterns.

Findings

This study classifies 129 countries into four clusters: Specials, Advanced, Intermediates and Primitives. Each cluster exhibits strengths and weaknesses in terms of innovation performance. Specials excel in the areas of institutions and knowledge commercialization, while the Advanced cluster demonstrates strengths in education and ICT-related services but shows weakness in patent commercialization. Intermediates show strengths in venture-capital and labour productivity but display weaknesses in R&D expenditure and the higher education quality. Primitives exhibit strength in creative activities but suffer from weaknesses in digital skills, education and training. Additionally, the study has identified 35 indicators that have negligible variance contributions across countries.

Originality/value

The study contributes to finding the relevant countries’ grouping for the enhancement of communication, integration and learning. To this end, this study highlights the innovation structural differences among countries and provides tailored innovation policies.

Details

Journal of Entrepreneurship and Public Policy, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2045-2101

Keywords

Article
Publication date: 9 October 2023

Rui Xu, Xiaoxuan Zhu, Yu Wang, Jibao Gu and Christian Felzensztein

Innovativeness is crucial for industrial cluster firms to gain sustained competitive advantage. This study aims to investigate the effects of inter-firm coopetition on firm…

Abstract

Purpose

Innovativeness is crucial for industrial cluster firms to gain sustained competitive advantage. This study aims to investigate the effects of inter-firm coopetition on firm innovativeness within a cluster and examines the moderating role of institutional support.

Design/methodology/approach

This research adopts an empirical survey method using multi-source data from 181 industrial cluster firms. Regression is used to test the hypotheses of this study.

Findings

The results show that cooperation and constructive conflict promote firm innovativeness, while destructive conflict is detrimental to firm innovativeness. Moreover, the study also finds that cooperation interacts with both types of conflict to affect firm innovativeness, where cooperation and constructive conflict interact negatively on firm innovativeness, while cooperation and destructive conflict interact positively on firm innovativeness. In addition, institutional support weakens the effects of cooperation and destructive conflict on innovativeness, respectively, but has no significant moderating effect on the relationship between constructive conflict and innovativeness.

Originality/value

These findings enrich the current research on coopetition. The interaction effects of cooperation and both types of conflict on innovativeness deepen the concept of coopetition and responds to the call to further explore the interaction effects within coopetition. The moderating role of institutional support fills a gap in the empirical research on the role of institutional factors affecting coopetition on innovation and also provides valuable suggestions for firm managers and governments in industrial clusters.

Details

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

Keywords

Article
Publication date: 15 September 2021

Jingda Ding, Chao Liu and Yiqing Yuan

This paper aims to explore the characteristics of knowledge diffusion of library and information science to reveal its development trend and influence on other disciplines.

Abstract

Purpose

This paper aims to explore the characteristics of knowledge diffusion of library and information science to reveal its development trend and influence on other disciplines.

Design/methodology/approach

Based on the ESI discipline classification, this paper measures the knowledge diffusion from the library and information science to other disciplines over the last 24 years using indicators in four dimensions: breadth, intensity, speed and theme of knowledge diffusion.

Findings

The results show that the knowledge diffusion breadth of library and information science is wide, spreading to 21 ESI disciplines; the knowledge spread mainly concentrates in four soft or applied disciplines, and yet partially inter-disciplinary, and the knowledge diffusion intensity to each ESI discipline is parabolic whose highest point is mostly in 2004–2005; the speed of spreading to the 21 ESI disciplines is faster and faster, and the articles at the highest speed of knowledge diffusion are basically published after 2005; the knowledge diffusion themes are becoming increasingly diverse, deepening and specialization over time.

Originality/value

This paper modifies the relevant indicators of knowledge diffusion and constructs a measurement framework of knowledge diffusion from four aspects: breadth, intensity, speed and theme. The research method can also be used to explore the characteristics of knowledge absorption of a discipline from other ones.

Article
Publication date: 22 January 2024

Kevin Escoz Barragan, Sohaib S. Hassan, Konrad Meisner and Levan Bzhalava

Digital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and…

Abstract

Purpose

Digital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and their impact on innovation in small and medium-sized enterprises (SMEs) remains understudied. Therefore, this study aims to investigate the impact of digital transformation stages on a differentiated measurement of innovation performance in SMEs.

Design/methodology/approach

The authors propose a simplified one-dimensional digital maturity path to estimate the stages of digital transformation in SMEs. The authors validate their approach with a cluster analysis and perform an ordered logistic regression to estimate the impact of digital transformation stages on SMEs' innovation performance.

Findings

The authors' results show that digital transformation in general has a positive impact on SMEs' innovation performance. More precisely, the authors find that the early stage of digital transformation has a detrimental effect on innovation performance, while significant and positive effects can be expected from the experimental stage onward. Furthermore, the advanced stage of digital transformation significantly increases the probability of producing radical innovations.

Originality/value

This study contributes to the ongoing discussion about the relationship between digital transformation and innovation in SMEs by presenting an approach to quantify digital transformation stages in SMEs. Additionally, this study provides new insights into the specific dynamics of the relationship between different stages of digital transformation and their impact on a differentiated measurement of innovation performance, including technological, non-technological and radical innovation.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 7 June 2022

Indranil Ghosh, Rabin K. Jana and Paritosh Pramanik

It is essential to validate whether a nation's economic strength always transpires into new business capacity. The present research strives to identify the key indicators to the…

Abstract

Purpose

It is essential to validate whether a nation's economic strength always transpires into new business capacity. The present research strives to identify the key indicators to the proxy new business ecosystem of countries and critically evaluate the similarity through the lens of advanced Fuzzy Clustering Frameworks over the years.

Design/methodology/approach

The authors use Fuzzy C Means, Type 2 Fuzzy C Means, Fuzzy Possibilistic C Means and Fuzzy Possibilistic Product Partition C Means Clustering algorithm to discover the inherent groupings of the considered countries in terms of intricate patterns of geospatial new business capacity during 2015–2018. Additionally, the authors propose a Particle Swarm Optimization driven Gradient Boosting Regression methodology to measure the influence of the underlying indicators for the overall surge in new business.

Findings

The Fuzzy Clustering frameworks suggest the existence of two clusters of nations across the years. Several developing countries have emerged to cater praiseworthy state of the new business ecosystem. The ease of running a business has appeared to be the most influential feature that governs the overall New Business Density.

Practical implications

It is of paramount practical importance to conduct a periodic review of nations' overall new business ecosystem to draw action plans to emphasize and augment the key enablers linked to new business growth. Countries found to lack new business capacity despite enjoying adequate economic strength can focus effectively on weaker dimensions.

Originality/value

The research proposes a robust systematic framework for new business capacity across different economies, indicating that economic strength does not necessarily transpire to equivalent new business capacity.

Details

Benchmarking: An International Journal, vol. 30 no. 4
Type: Research Article
ISSN: 1463-5771

Keywords

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