Search results

This research aims to investigate how organisational and contextual factors affect supply chain collaboration and how that, in turn, influences firms' competitive advantage in the garment industry in the context of Vietnam, a developing country.

Following a qualitative research design, in-depth interviews were conducted with senior managers who are involved in supply chain collaboration in twelve garment companies in Vietnam. The data were recorded, transcribed and analysed using NVivo 12. Based on the literature and interview findings, a research model underpinned by the relational view (RV) and institutional theories, with organisational and contextual factors being the antecedents and competitive advantage as the outcome of supply chain collaboration, was proposed.

The findings showed that organisational and contextual factors induce both internal, supplier and customer supply chain collaboration. There is also a positive relationship between supply chain collaboration and competitive advantage. Based on these findings, a strategy matrix for supply chain collaboration is also put forward.

This is one of the first empirical attempts to investigate the role of organisational and contextual factors as potential antecedents of supply chain collaboration and its effects on competitive advantage in the garment industry. The research is expected to enrich both the literature and management practices on supply chain collaboration in the context of developing countries.

Introduction: Investments in environmentally friendly initiatives can bolster infrastructure projects, agricultural methods, and water management systems that improve the ability to withstand climate-related difficulties. Green investments encompass endorsing carbon markets and financial instruments that incentivise reducing emissions. This research helps attain the climate objectives described in sustainable development goal 13 (SDG 13).

Purpose: This chapter aims to investigate the relationship between greenhouse gases (GHG) and gross domestic product (GDP), with the underlying objective of understanding the relevance of green investment for sustainable development.

Methodology: For the analysis, the top five countries: the USA, China, Germany, Japan, and India, were chosen based on the world’s largest economies in 2023, as per their GDP data. For testing the hypothesis, data from the World Bank database during the period 2002-2022 was retrieved and GDP is used as a dependent variable and GHG as an independent variable. For the study, panel data are used, and the Johansen cointegration test and ordinary least squares (OLS) regression are applied.

Findings: In the case of China and India, the null hypothesis has been rejected, which is depicted by the significant and high degree of relation between GHGs and the GDP of these two countries. The null hypothesis is also rejected for the USA and Germany, but it shows a significant and moderate degree of relationship between GHG and GDP. For Japan, the null hypothesis is accepted and reflects a negative relationship between GHG and GDP.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

Quiet quitting (QQ) has been around in academic and professional discussions for quite some time. Despite many definitions and reasons for QQ being proposed, no study has systematically undertaken exploratory research on the reasons and behavioral manifestations of employees who QQ.

Our study adopted an exploratory qualitative approach to understand the reasons for QQ without any prior judgment. We undertook interviews with 21 employees who have undergone QQ in their careers.

The qualitative inquiry brought out the reasons behind QQ. The three major reasons for QQ were identified by thematic analysis: personal, organizational politics and people dynamics. Further, overt and covert behaviors that employees undertake while QQ were framed with this study. Findings are discussed in the framework of existence, relatedness and growth (ERG) theory.

The theoretical implications include understanding QQ within the purview of ERG theory and the frustrated regression model. Practically, measures to be taken by organizations to mitigate the instances are presented, along with employee suggestions on what measures need to be taken to avoid QQ behaviors of employees.

This study is a pioneering work on the exploratory understanding of QQ. Empirically validated theory formulation of QQ is developed here for the first time.

The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

Prior institutional duality research asserts that ceremonial implementation of organisational practice protects multinational corporations’ subsidiaries. However, the temporal dynamics of the safeguarding function has been under researched. Public sector organisations have also been ignored. This research aims to explore how the safeguarding function is created, maintained and disrupted using the overseas offices (OOs) of a bilateral development agency (BDA) as a case.

A multi-case study, underpinned by neo-institutionalism, was conducted. Data obtained from in-depth remote interviews with 39 informants from the BDA OOs were analysed using the “asking small and large questions” technique, four analytical techniques, cross-case synthesis and theoretical propositions.

A three-phase process was identified. The first phase is the appearance of discrepancies due to institutional duality. The second is the emergence of ceremonial implementation as a solution. In the third phase, “the creation, maintenance and disruption of a safeguarding function” begins. When ceremonial implementation successfully protects the OOs, the safeguarding function is created. The OOs are likely to repeat ceremonial implementation, thus sustaining the function. Meanwhile, when conditions such as management staff change, ceremonial implementation may not take place, and the safeguarding function disappears.

The BDA OOs may not face strong host country regulative pressures because they are donors to aid-recipient countries. Hence, the findings may not directly apply to other public sector organisations.

Development cooperation practitioners should understand that ceremonial implementation is not exclusively harmful.

To the best of the author’s knowledge, this is the first institutional duality research that explores the temporal dynamics of safeguarding functions targeting public sector organisations.

The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.