Research on the evaluation and impact trends of China's skill talent ecosystem in the digital era - An analysis based on neural network models and PVAR models

This study develops a "Skill Talent Ecological Evaluation Model" across cultivation, potential energy, kinetic energy, innovation, and service and support ecologies. AHP-entropy determines indicator weights, Hopfield neural network assesses talent ecology levels, and the PVAR model analyzes digital transformation effects. Findings reveal: Cultivation ecology rates A, potential ecology rates B+, kinetic ecology rates B-, service and support ecology rates B-, and innovation ecology rates C. Digital transformation spurs skill demand, impacting talent and economic contributions. Kinetic ecology sees increased demand, potentially impacting traditional industries positively. Innovation ecology necessitates continuous skill learning. Service and support ecology witnesses growth in digital entrepreneurship, requiring policy incentives and incubation center support.

The impact of digital transformation on green total factor productivity of heavily polluting enterprises

Introduction

Digital transformation has become an important engine for economic high-quality development and environment high-level protection. However, green total factor productivity (GTFP), as an indicator that comprehensively reflects economic and environmental benefits, there is a lack of studies that analyze the effect of digital transformation on heavily polluting enterprises' GTFP from a micro perspective, and its impact mechanism is still unclear. Therefore, we aim to study the impact of digital transformation on heavily polluting enterprises' GTFP and its mechanism, and explore the heterogeneity of its impact.

Methods

We use Chinese A-share listed enterprises in the heavily polluting industry data from 2007 to 2019, measure enterprise digital transformation indicator using text analysis, and measure enterprise GTFP indicator using the GML index based on SBM directional distance function, to investigate the impact of digital transformation on heavily polluting enterprises' GTFP.

Results

Digital transformation can significantly enhance heavily polluting enterprises' GTFP, and this finding still holds after considering the endogenous problem and conducting robustness tests. Digital transformation can enhance heavily polluting enterprises' GTFP by promoting green innovation, improving management efficiency, and reducing external transaction costs. The improvement role of digital transformation on heavily polluting enterprises' GTFP is more obvious in the samples of non-state-owned enterprises, non-high-tech industries, and the eastern region. Compared with blockchain technology, artificial intelligence technology, cloud computing technology, big data technology, and digital technology application can significantly improve heavily polluting enterprises' GTFP.

Discussion

Our paper breaks through the limitations of existing research, which not only theoretically enriches the literature related to digital transformation and GTFP, but also practically provides policy implications for continuously promoting heavily polluting enterprises' digital transformation and facilitating their high-quality development.

Exploring the critical factors influencing the outlying island talent recruitment and selection evaluation model: Empirical evidence from Penghu, Taiwan

The declining birth rate, population ageing and the outbound migration of young people in recent years have created obstacles for local companies in recruiting skilled personnel. Similar factors have also affected recruitment in the outlying island of Penghu, Taiwan. Multiple-attribute decision-making (MADM) techniques for evaluation model development were incorporated in the present research to investigate the key determinants of talent recruitment by the coffee house operators in the outlying island of Penghu. The decision-making trial and evaluation laboratory (DEMATEL) approach was first implemented to (i) examine the extent of mutual impact between evaluation predictors and (ii) develop an influential network relation map (NRM) for indicator and sub-indicator assessments. Subsequently, the DEMATEL-based analytic network process (DANP) technique was utilised to compute the weights of every indicator and sub-indicator. Additionally, the modified VlseKriterijumska Optimizcija I Kaompromisno Resenje or VIKOR (in Serbian) approach evaluated every indicator and sub-indicator performance gap for empirical data analysis with suggested conclusions and recommendations. The results showed that the first dimension that should be improved is past experience, followed by personality traits and professional competence, according to the causal diagram of the NRM. The weights acquired through the DANP indicated that professional competence was the most influential dimension, followed by personality traits, whereas past experience was the least influential. Past experience revealed that the highest gap value is the most important for development if coffee house operators want to attain the appropriate levels. Finally, the theoretical, managerial, and practical implications were discussed. Notably, the research outcomes could optimise talent recruitment, establish appropriate talent recruitment methods for coffee house operators, and enhance the human resource quality in the outlying island of Penghu.

How digitalization reinvented entrepreneurial resilience during COVID-19

The emergence of new digital technologies has transformed entrepreneurship and, very likely, enabled many new and established ventures to avoid bankruptcy throughout the COVID-19 pandemic. Digital technologies are key to identifying, evaluating and exploiting opportunities, scaling a venture's competitiveness, improving efficiency and innovating, especially during uncertain times. We explore how digital technologies reinvented entrepreneurial resilience during the COVID-19 pandemic and distill the digital artifacts, platforms and infrastructures used by entrepreneurs to demonstrate entrepreneurial resilience. We analyzed 42 reflective interviews featuring successful entrepreneurs from How I Built This "Resilience Series" podcast that explore how entrepreneurs responded to the COVID-19 crisis. We adopted a systematic approach to identify and describe the behaviors, actions and strategies related to digitalization to reinvent the business in the uncertain and resource-constrained context of COVID-19. The data analysis yielded thirteen first-order codes categorized into five second-order themes: creative digital pivoting, digital infrastructures, social impact through digital technology, burdens to digital adoption, and growth through digitalization. These second-order themes reveal to function as both enablers and barriers to entrepreneurial resilience in this adverse context. Our exploratory analysis suggests how digitalization influences entrepreneurial resilience.

Occupation-specific recruitment: An empirical investigation on job seekers’ occupational (non-)fit, employer image, and employer attractiveness

Organizations may need to attract occupational groups they did not recruit so far to implement strategic changes (e.g., digital transformation). Against the backdrop of this practical problem, this study introduces and explores an occupation-based measure of person-organization fit: occupational fit. I investigate its relationship with employer attractiveness based on human capital theory and explore the role of employer image as a moderator in this relationship. I surveyed 153 software engineers and mechanical engineers to analyze whether their occupational fit with software engineering and mechanical engineering firms is related to employer attractiveness. I find that occupational fit is only related to a firm’s employer attractiveness among software engineers. Employer image does not moderate this relationship. A qualitative follow-up study proposes first explanations for the unexpected differences between the two occupations by indicating that occupations may differ in the logic they apply to determine fit and their degree of professionalization. The study contributes to research by highlighting the neglected role of occupation in recruitment research and exploring potential boundary conditions of recruitment for fit. Implications for future research and practice are discussed.

Human resource transformation in India: examining the role digital human resource technology and human resource role

Purpose

Human resource (HR) transformation research has not studied the role of digital HR technology and HR role in the context of Indian organisations. To address the gap, the current research aims to investigate the impact of HR role and digital HR technology on successful HR transformation. Further, the research shall investigate the mediating role of various HR roles (i.e. administrative, employee champion, change agent and strategic partner role) on digital HR technology and business outcomes.

Design/methodology/approach

The research used a post-positivist methodology using survey method. Data has been collected from 918 executives representing several sectors of Fortune 500 Indian companies. Validated instrument has been used and the collected data are analysed using AMOS and structural equation modelling.

Findings

HR transformation using Digital human resource technology (HRT) can significantly enhance business outcome of fortune 500 companies of India if it is mediated by different HR role (strategic, employee champion, change agent and administrative expert). The result also proved that just implementation and adaption of the Digital HRT may not guarantee HR Transformation unless HR optimise the specific role as per the need of the hour.

Originality/value

HR transformation research has not studied the role of digital HR technology and HR role in the context of fortune 500 Indian organisations.

Skills and competencies for digital transformation – a critical analysis in the context of robotic process automation

Purpose

Digital transformation of organizations has major implications for required skills and competencies of the workforce, both as a prerequisite for implementation, and, as a consequence of the transformation. The purpose of this study is to analyze required skills and competencies for digital transformation using the context of robotic process automation (RPA) as an example.

Design/methodology/approach

This study is based on an explorative, thematic coding analysis of 119 job advertisements related to RPA. The data was collected from major online job platforms, qualitatively coded and subsequently analyzed quantitatively.

Findings

The research highlights the general importance of specific skills and competencies for digital transformation and shows a gap between available skills and required skills. Moreover, it is concluded that reskilling the existing workforce might be difficult. Many emerging positions can be found in the consulting sector, which raises questions about the permanent vs temporary nature of the requirements, as well as the difficulty of acquiring the required knowledge.

Originality/value

This paper contributes to knowledge by providing new empirical findings and a novel perspective to the ongoing discussion of digital skills, employment effects and reskilling demands of the existing workforce owing to recent technological developments and automation in the overall context of digital transformation.