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Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

The paper aims to provide an understanding of how the transfer of progressive human resource management (HRM) practices may or may fail to render multinational enterprises (MNEs) institutional entrepreneurs creating change in job quality and decent work to underprivileged workers in the low-pay retail sector in Japan.

The paper draws on survey questionnaire data and interviews with workers and management in a foreign retailer in Japan.

The findings suggest that even where MNEs may provide some measurable material improvements in job quality, in this case equal pay for equal work, the total outcomes are nevertheless shaped by institutional context and constraints. In this case, the improvement in pay was intertwined with flexibility demands that were possible to meet for some workers but not others. In particular, women with care responsibilities and competing demands on their time were not able to experience “decent work” in the same way as others.

The study had a relatively low response rate, due to lack of discretion over time experienced by workers in Japan, as well as limited data on program outcomes, with interviews conducted with a small number of participants.

The study suggests that spaces and opportunities exist for MNEs to diverge from dominant practices in given host country locations and exercise a level of agency as emissaries of decent work but successful outcomes require a very thorough understanding of individual worker experiences within the institutional constraints of given environments.

The study offers insights into the complexities of initiatives by MNEs to contribute to the provision of decent work, particularly for workers in underprivileged positions including women in low-pay sectors such as retail, as firm-level practices lead to variable outcomes when filtered through local institutions.

The study brings together a focus on firm-level practices that inform much of the international HRM and international management scholarship with an emphasis on the experiences of workers, which is pursued in the sociology of work, to investigate whether MNEs can be actors in the realising of the Sustainable Development Goals around decent work.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

Realistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.

The vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.

Several models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.

The VBI models for compound heavy vehicles are 2-D.

This work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.

The structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

A flexible manufacturing system (FMS) helps improve the system’s performance, thus increasing its overall competitiveness. FMS is an essential component of Industry 4.0 (I4.0), which has revolutionized the way firms manufacture their products. This study aims to investigate the diverse focus of the research being published over the years and the direction of scholarly work in applying FMSs in business and management.

A total of 1,096 bibliometric data were extracted from the Scopus database from the years 2001 to 2021. A systematic review and bibliometric analysis were performed on the data and related articles for performance measurement and scientific mapping on the FMS themes.

Based on co-keyword, the study reveals four major themes in the FMS field: mathematical models and quantitative techniques, scheduling and optimization techniques, cellular manufacturing and decision-making in FMSs. Based on bibliometric coupling on 2018–2021 bibliometric data, four themes emerged for future research: scheduling problems in FMS, manufacturing cell formation problem, interplay of FMS with other latest technologies and I4.0 and FMS.

The originality lies in answering the following research questions: What are the most highlighting themes in FMS, and how have they evolved over the past 20 years (2001–2021)? What topics have been at the forefront of research in FMS in the past five years (2016–2021)? What are the promising avenues of research in FMS?

Working capital efficiency (WCE) is crucial for the sustainability of both large and small firms. This study aims to use the sample of micro, small and medium-sized enterprises (MSMEs) in India and tries to understand the critical determinants of WCE.

Using a fixed effect panel data model on a sample of 578 MSMEs (59 micro, 226 medium and 296 small firms), this study explores the relationship between the predictors of WCE. Additionally, the study adopted two metrics for measuring WCE among each type of firm (micro, small and medium).

Several firm-specific variables, including leverage (lever), firm age (AGE), firm size (Fsiz), profitability (Prof), extended payment terms (EPT), human capital (HCap), asset turnover ratio (ATR), reverse factoring (RF) and firm growth (FG), have a significant effect on working capital management efficiency (WCE). In contrast, tangibility (Tangib) and salary expenses (Sal) had an insignificant effect on working capital management efficiency.

The study is based on secondary data. Future studies may incorporate some primary data, which will facilitate qualitative analysis.

The studies explore the relationship between WCE and expenses in HCap, EPT, RF and Sal as the predictors for WCE, which was not studied earlier in MSMEs scenario, especially in case of developing nation.

This paper aims to investigate the drivers of foreign direct divestment (FDD), how it relates to foreign direct investment (FDI) flows and stocks and its implications for developing countries. While divestment occurs for various reasons, it can be explained by reversing the propositions implied by FDI theories.

The authors combine FDI data and FDI theories to provide theoretical explanations for FDD and what it means for developing countries. FDI stock and flow data are used to derive inferences on trends in FDD and examine the implications of FDI theories on FDD.

Changes in the modes of global production and the rise of COVID-19 have reinforced the trend of stagnant or diminishing FDI flows observed since the global financial crisis, with implications for FDD. The authors demonstrate how the various FDI theories can be used to explain FDD, except for the currency areas hypothesis. By reviewing the costs and benefits of FDI, it is concluded that shrinking FDI flows and stocks may not be as detrimental for developing economies as it is typically portrayed.

The paper uses two original approaches to measure and explain the motives for FDD. The first is a reassessment of FDI theories in a way that makes them valid theories for FDD. The second original approach is to interpret data on FDI flows and stocks to imply the trends governing FDD, which is useful, as data on foreign divestment are not available on a country or regional basis.

The purpose of this study was to develop and psychometrically evaluate a tool to measure nurses’ knowledge of, attitude toward and practice regarding the elder abuse phenomenon.

This study is methodological research that was conducted in two stages. In the first stage, the available tools related to knowledge, attitude and practice regarding elder abuse were identified through an extensive review of theoretical principles, previous research and related articles. The initial items were extracted and categorized. In the second stage, the psychometric properties of the tool were examined by assessing face validity, content validity, construct validity, internal consistency and stability. Four hundred nurses working in the hospitals of Shahid Beheshti University of Medical Sciences were selected to determine the validity of the structure.

The initial tool consisted of 114 items extracted, 34 of which were removed according to the experts’ opinions. The face validity of the 80-item questionnaire was then examined. The number of the items was reduced to 74 after measuring the face validity. After the qualitative measurement of the content validity, three items were eliminated. After measuring the content validity using a quantitative approach, 8 other items were removed and 63 items remained. According to the content validity index, 1 more item was removed, reducing the number to 62. The construct validity approved three dimensions of knowledge, attitude and practice. Cronbach's alpha coefficient was calculated to be 0.84. The stability was also confirmed through test–retest with a value of 0.85 for the whole scale.

The tool developed for assessing nurses’ knowledge, attitude and practice regarding elder abuse has acceptable dependability and credibility. Given the advantages of this tool, including the simplicity and fluency of sentences, and the comprehensive evaluation of nurses’ knowledge, attitude and practice regarding elder abuse, it is recommended to use this tool in relevant future studies.

This case teaches students the importance of maintaining a strong FICO score by illustrating the consequences of paying bills late or not at all. The protagonist is David Molina, a waiter at a struggling Italian restaurant located down the block from where he lives. Money is tight for Molina right now—his limited income means he lives paycheck to paycheck. However, Molina knows things will be looking up for him soon because he recently accepted a job as a bank teller across town—his first desk job.

Molina has been putting off paying two of his bills: a cable bill and his Bank of America credit card bill, both of which are late and have been issued, this time, in the form of threats to impact Molina's credit score if he doesn't pay them. He has just enough money to afford the minimum payments on each overdue bill. But then he receives a phone call from his friend, Jim Lindsey, reminding him about an invitation to go to Myrtle Beach for the upcoming weekend. Molina knows he cannot afford it, but a woman he's attracted to, Jessica, will be there too. Should Molina put off the bills yet again, and if so, how exactly will being late on them hurt his credit score?