Modelling the barriers of talent agility in Indian automobile industry in the era of Industry 4.0

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-03-20 DOI:10.1108/jm2-06-2023-0124
Gopal Krushna Gouda, Binita Tiwari
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Abstract

Purpose

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

Design/methodology/approach

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

Findings

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

Practical implications

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

Originality/value

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

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工业 4.0 时代印度汽车行业人才敏捷性的障碍建模
目的 COVID-19 的爆发破坏了商业环境,严重影响了员工的士气和工作表现。此外,印度汽车行业在 COVID-19 疫情中遭受了重大挫折,受到了严重影响。人才敏捷性是人力资源管理领域的一个新兴概念,它将促进汽车行业的创新和生产力。因此,本研究旨在探索新常态下印度汽车行业内部敏捷人才建设的障碍。设计/方法/途径通过文献综述确定了人才敏捷性的障碍,并通过专家意见进行了验证。本研究采用了一种混合方法,将解释性结构建模-极性(ISM-P)与决策试验和评估实验室(DEMATEL)相结合,建立了障碍的分层结构模型,然后将其分为因果组。研究结果采用多种方法研究的结果表明,技能和能力短缺、缺乏信息技术基础设施、缺乏灵活应变的领导者、缺乏智能人力资源管理技术和实践、缺乏有吸引力的奖励制度/职业管理、先进的技术和研发能力差、行业和机构之间的衔接差以及资金限制是关键障碍。原创性/价值本研究的独特之处在于,它以印度汽车行业为背景,探讨了在当前快速变化的环境中与人才敏捷性相关的当代问题。本研究就人才敏捷性的障碍制定了一个整体综合的 ISM(P)-DEMATEL 层次框架,表明了内部依赖权重,即障碍之间相互关系的强度。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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