Search results

The primary objective of this research is to determine critical success factors (CSFs) that enable textile enterprises to effectively implement Kaizen, a Japanese concept of continuous development, particularly during disruptive situations. The study aims to provide insights into how Kaizen is specifically employed within the textile sector and to offer guidance for addressing future crises.

This study employs a structured approach to determine CSFs for successful Kaizen implementation in the textile industry. The Triple Helix Actors structure, comprising business, academia and government representatives, is utilized to uncover essential insights. Additionally, the Matriced Impacts Croises-Multiplication Applique and Classement (MICMAC) analysis and interpretative structural modeling (ISM) techniques are applied to evaluate the influence of CSFs.

The research identifies 17 CSFs for successful Kaizen implementation in the textile industry through a comprehensive literature review and expert input. These factors are organized into a hierarchical structure with 5 distinct levels. Additionally, the application of the MICMAC analysis reveals three clusters of CSFs: linkage, dependent and independent, highlighting their interdependencies and impact.

Major contribution of this study is understanding how Kaizen can be effectively utilized in the textile industry, especially during disruptive events. The combination of the Triple Helix Actors structure, MICMAC analysis and ISM provides a unique perspective on the essential factors driving successful Kaizen implementation. The identification of CSFs and their categorization into clusters offer valuable insights for practitioners, policymakers and academia seeking to enhance the resilience and sustainability of the textile industry.

This study aims to investigate the direct effect of organizational reputation (OR), employer brand (EB) and organizational attributes (OA) on the intention to apply for a job vacancy (IAJV); further, it examined the mediating effect of employer brand in the OA-IAJV relationship while taking into consideration the moderating effect of organizational reputation.

A mixed-method approach was employed, with ten in-depth interviews followed by a questionnaire with additional 356 job seekers in Cairo and Giza cities; the sample includes both fresh graduates and experienced job applicants in the job market. The qualitative analysis confirmed that some respondents use organizational reputation as a signal of its performance. The path analysis technique tests the research hypotheses using a partial least squares structural equation modeling (PLS-SEM).

The findings revealed that the most influential variable in the intention to apply is organizational attributes, followed by organizational reputation and finally employer brand. There is a significant relationship between organizational attributes and intention to apply for a job vacancy via employer brand. In addition, the results indicate a noteworthy moderating impact of organizational reputation on the association between employer brand intentions to apply for a job and the relationship between organizational attributes and intention to apply for a job opening.

To the best of the authors’ knowledge, this study contributes to the understanding of the direct and indirect effects of organizational reputation and organizational attributes on intention to apply through the mediating role of the employer brand. This research opens new avenues for recruitment research, considering the moderating effect of organizational reputation on strengthening the impact of the independent variables on the intention to apply and the interaction between the variables affecting the intention. Further, this study focuses on the needs of the job applicants when perceiving the organizational factors and identifies which signals can generate the intention to apply according to the signaling theory.

Consumer choice theory (CCT) and the law of diminishing marginal utility help to explain shoppers that value less and prioritize needs. Additional units provide a marginal return on investment. Buying more does not mean equivalent gains for additional money spent. The researchers developed and validated the necessity shopper scale (NSS) to study need-focused shoppers.

The researchers followed standard psychometric practices to create and validate the NSS. The researchers performed item development, data collection, exploratory analysis, confirmatory factor analysis, and predictive validity analysis using survey data (N = 1,266).

Discriminant and convergent validity analyses demonstrated that the measure was distinct from existing measures. Predictive validity analysis found necessity shoppers (NS) are more likely to buy one over buy one get one half off (BOGOHO). NS were associated with a higher connection to community/group (CTCG). Higher hyperopia (i.e. disinclination to indulgence) with necessity shopping beliefs heightened this CTCG. A higher CTCG was associated with a greater likelihood to select BOGOHO.

NS (more connected to others) buy more to share with others, while buying just enough for themselves. Social connections are long-term investments involving more people and more needs to fulfill. Brands marketed with communal values and able to enhance social connections are discussed as implications to encourage NS to buy more.

This study develops a measure of anticipated chilly climate for women and provides initial evidence of its validity.

We draw on three studies. Study 1 consisted of three focus groups to gain deeper insights into the meaning of the concept for prospective female jobseekers and generate scale items. In Study 2, we pre-tested job post vignettes (N = 203), refined the scale items and explored the factor structure (N = 136). Study 3 aimed to determine the convergent and discriminant validity of the new scale (N = 224) by testing its relationships with organisational attractiveness, person-organisation fit perceptions and gendered language.

The results show that the anticipated chilly climate is an important concept with implications for applicants’ career decision-making and career growth in the technology industry, where women tend to be underrepresented. Perceptions of anticipated chilly climate comprise expectations of devaluation, marginalisation and exclusion from the prospective employment. The masculine stereotypes embedded in the language of the job posts signalled a chilly climate for both genders, negatively affecting perceptions of fit and organisational attractiveness.

Most previous studies have focussed on the actual experiences of chilly climates in organisations. We extend this body of literature to anticipatory climates and draw on social identity threat theory and signalling theory to highlight that job applicants make inferences about the climate they expect to find based on job ads. Specifically, they may anticipate a chilly climate based on cues from job ads signalling masculine stereotypes. Whilst the literature has emphasised women’s perceptions of chilly climates within organisations, our results show that both genders anticipate chilly climates with detrimental consequences for both organisations and prospective job applications.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

Impulsive buyers are a dream segment for retailers and marketers. Stimulants in the retail environment and cognitive aspects evoke a sudden urge the acquisition of products spontaneously. This paper aims to examine key cognitive aspects of impulsive buying behaviour and purchase regret in an online context.

An online survey was conducted to collect the data of 317 online consumers with the help of a convenience sampling technique. The structural equation modelling technique was carried out to establish the validity and reliability of measures and examine the proposed relational paths.

The study results suggest that cognitive aspects recede impulsive buying, resulting in purchase regret. The empirical findings on the impulsive buying behaviour and purchase regret to yield several important implications, including developing marketing strategies and policies to evoke the intentions for impulsive buying behaviour, consumer innovation and balancing the feelings of regret.

The study also provides some significant contributions to the literature on online impulse buying and its related paradigms.

This study mainly attempted to determine the precursors of online impulse buying and purchase regret from the perspective of hedonic and experiential consumption motivation and consumer innovation. Getting reflections from cognitive dissonance theory and the post purchase evaluation, a theoretical model was developed and empirically tested for impulsive online buyers.

This study aims to investigate the impact of customer perceptions of Augmented Reality (AR) attributes such as augmentation, interactivity and vividness on attitudes towards AR mobile apps, virtual product and behavioural intentions. Also, the mediation role of customer engagement in the effect of perceptions of AR attributes on attitudes and behavioural intentions is examined using the Theory of Interactive Media Effects.

The study used a cross-sectional design. A total of 456 valid data were collected from the Millennials and Generation Z cohorts using purposive sampling. The conceptual framework was assessed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Partial Least Squares-Multi Group Analysis (PLS-MGA).

The research revealed that customer perceptions of AR features such as augmentation, interactivity and vividness significantly influenced customer engagement, leading to favourable attitudes towards both the AR mobile app and the Virtual product as well as behavioural intentions. Furthermore, the study substantiates the role of customer engagement as a mediator in the relationship between customer perceptions of AR attributes and both attitudinal and behavioural outcomes.

To the best of the authors’ knowledge, this is the first attempt to investigate the significance of perceived augmentation as an antecedent to customer engagement and the mediating role of customer engagement on the influence of perceptions of AR attributes on attitudinal and behavioural intention.

The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.

In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.

The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.

The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.