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The purpose of this research is to identify the current reverse logistics practices adopted by soft drink companies and the prominent factors which can decide the efficiency and effectiveness of the entire process of the reverse logistics channel. The paper employs Pareto analysis and the Analytical Hierarchy Process (AHP) method on data collected from logistics professionals involved in the software industry in Sri Lanka using two questionnaires. As the prominent factors, transportation, accidents, packaging, a method of storage, the cleaning process and sorting process was identified and the first four prominent factors have a higher influence on both measures of efficiency and effectiveness. They can also identify the external factors which can emerge inefficiencies due to outsourced dealers. Lack of previous literature on the subject matter and the difficulty to access the filed data were the main limitations of this study. The identified factors will help to identify the correct root causes for the inefficiencies of the current reverse logistics practices and concentrating on these factors will give an opportunity for the soft drink industry players to successfully implement a sustainable green supply chain which reduces waste at each stage of its forwards and reverse logistics process. Transportation, Accidents, Packaging, and Storage have been previously identified as considerations in reverse logistics processes and the current study showed that they have a higher impact on both efficiency and effectiveness on reverse logistics and these factors should be given specific consideration while in the operations.

Given the significant role played by valuers and the evidence of a lack of independence in some property valuation industries, particularly in emerging markets, this study analyses the issue of client influence in property valuation by providing a valuer-client perspective and measuring the interrelationships between the clients' influence factors to identify causal factors of prominence, which can assist in developing solutions to address the clients' influence issue.

The study used a mixed-method approach. Firstly, interviews were conducted with ten property valuers and five financial institution staff in Nigeria, and the data were subject to thematic analysis using Nvivo 12 software. A matrix questionnaire survey was administered to the valuers, and the responses were analysed using the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The results indicate that institutional clients, loan-seeking customers, property valuers and the perception of corruption within the Nigerian environment fuelled the issue of clients' influence. Based on the measurement of the interrelationship between the 14 identified client influence factors, the type of company, perception the public has of the industry, size of the firm, relationship with the client, type of client and regulatory framework were the factors of prominence.

The findings of this study bear huge implications for Nigeria and other similar structured property markets facing the issue of clients' influence in property valuation. With the prominent factors bearing root in a mix of client, valuer and environmental factors such as the valuation structure, process and public perception, there is a need for solutions that level the playing field between institutional clients and valuers, reinforce transparency and establish excellent regulatory standards to address the issue of clients' influence.

This study is the first to measure the interrelationships between the clients' influence factors to identify the prominent causal factors. Accordingly, considering the multi-factors, the research is novel as it focusses on those factors that would likely lead to other factors, thereby providing opportunities to develop solutions that focus on those factors of prominence. Secondly, the study deviates from the narrative on clients' influence in property valuation, which pits it as solely a client or valuer factor, by showing how the interplay of the stakeholders' interests and the environment promotes the issue in a non-transparent property market.

The nature of construction works has a negative impact on physical, mental and emotional well-being and makes it difficult for the construction industry to attack and retain its workforce. The current study seeks to integrate the current knowledge focused on work–life balance (WLB) in the industry into an understandable whole.

An interpretivist philosophical approach was adopted using a bibliometric review and a narrative review of existing studies from both Scopus and Google databases. The Visualisation of Similarities viewer (VOSviewer) was used to prepare co-occurrence maps from the bibliographic data garnered.

The study reveals that the prominent factors influencing the WLB of the construction workforce are organizational culture, salary earned, heavy workload, long working hours and inflexible working time. The recent WLB discourse is on organisational commitment, job satisfaction and workplace dynamics. While WLB areas for further exploration are job stress, safety performance, employee attrition and an ageing workforce. Meanwhile, Africa and South America are still lagging in WLB research.

The findings reported here will assist stakeholders in identifying appropriate WLB initiatives that can be used to improve the well-being of the construction workforce. Also, the gaps in knowledge for further research were highlighted.

The findings reveal current trends and a road map for future studies on WLB in construction. It also reveals prominent factors influencing the WLB of workers in the construction industry.

Increasing environmental pollution and destruction have damaged sustainability in polluting societies and organizations. For this reason, various environmental management programs have recently been developed by governments and the environmental protection agencies (EPAs) to control and reduce environmental pollution. One of these programs is environmental audit (EA), which has not been fully implemented yet in developing countries and it has many disadvantages. It seems that one of the reasons for the lack of perfect implementation of EA in these countries is the lack of adequate review of the factors affecting its implementation. The purpose of this paper is to identify the factors affecting EA implementation and then analysis of causal relationships between them.

Decision-making trial and evaluation laboratory (DEMATEL) technique was used to identify causal relationships of factors affecting EA implementation. Based on the literature review and interviewing nine experts in EA, 17 factors were identified to influence EA implementation. Six experts were selected for the evaluation using snowball sampling method. Using the DEMATEL approach, a cause and effect relationship diagram was generated through which the effect of factors was analyzed.

Seventeen factors were categorized in terms of cause and effect, and the interrelationships of factors were also analyzed. “Reducing environmental impacts and improving environmental performance,” “obtaining environmental management system certificates” and “contributing to the environmental goals of investment projects” are the most prominent factors on the basis of their prominent score.

Analysis in the research is highly dependent on expert judgments and opinions may be biased. However, the initial matrix obtained from the experts is hindered by the ambiguity about some relationships. But this can be improved by using fuzzy and gray set theories. The factors used for the analysis may not be comprehensive and other researchers may find other factors.

Identifying the factors affecting EA implementation and analyzing causal relationships between them can be a guide and help governments to improve the implementation of EA or even develop this policy by being aware of the effect of the factors analyzed.

To the best of the authors’ knowledge, no comprehensive study of factors affecting EA implementation has been undertaken; this study is the first to identify these factors and analyze them using DEMATEL. Therefore, it is suggested that governments and EPAs in the policies focus on the significant factors.

The purpose of this paper is to examine different possible issues that can contribute to cost escalations in pharmaceutical capital projects and identify remedial measures to control them.

The study is supported by a detailed survey performed around some big pharmaceutical companies of Indian sub-continent. Various issues that lead to cost escalations of projects have been identified. Both exploratory and confirmatory factor analyzes have been applied to the feedback data.

The study derived and validated four prominent factors leading to cost escalations. The paper discusses these factors along with the remedies identified to control project cost. Convergent and discriminant validity have been well established for the model.

The data needful for the study were collected from only four big pharmaceutical companies established in Southern India. Through frequent and elaborate interactions with senior project managers of those companies, it became possible to get a consolidated list of reasons that contribute to cost escalations in pharmaceutical projects. The list and the feedback data may be more exhaustive if some more companies would have been surveyed. Future research is committed to cover some more notable pharmaceutical companies both nationally and internationally and enhance the sample size. This would add more strength to the analysis and derive more consistent and validated results.

This study provides necessary support to the project people to analyze different issues that stand as hurdles for project success and enable them to look for remedies to resolve them. The results would help the project managers to enhance their awareness in controlling the project costs. The study stands as a stepping stone and a roadmap to embed further research in this direction.

Since several significant issues impact the progress of the projects, this work focussed on analyzing cost-related issues in projects; since there is meager research work done in the area of pharmaceutical project management, as an attempt to fill the research gap, this work carried out detailed analysis of different issues leading to cost escalations in pharmaceutical capital projects; factor analysis, including exploratory and confirmatory, has been applied to the data collected and several important factors derived and the measurement model well validated; the research work was done in close interaction with the project people working in four big Indian pharmaceutical companies and useful information was collected; in addition to generation of important factors from the factor analysis, the study was further extended to collect various remedies to mitigate the issues that lead to cost escalations; the research work has enough strength to act as a role model to motivate researchers and project people to further their research on other issues and also refine the present work.

The objective of the study was to explore the factors influencing the career decisions of students doing the students' undergraduate (UG) and postgraduate (PG) programmes from urban private universities/colleges in the Indian context.

Career decision-making is determined by different factors and is contextual. The present study explores and identifies the prominent factors influencing career decision-making. A pool of 33 questions was developed, and these questions were initially administered to a sample of 233 students. Principal component analysis with Varimax Rotation identified salient factors. In the second study, confirmatory analysis was performed based on the opinion of 304 students.

The study shows that the student's career deciding factors include (1) career clarity, (2) career exploration, (3) career reward and recognition and (4) career initiative for professional and personal growth.

The study focussed on career-deciding factors for UG and PG students from urban universities/colleges in the Indian context and the findings can be used for planning career counselling interventions.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

The purpose of this study is to review the critical success factors (CSFs) of street food destinations, given the limited attention awarded to such research in the food and beverage sector.

An interpretivist approach was used to merge CSFs from street vending and culinary tourism perspectives, to develop a draft framework for analysing CSFs for street food destinations. In total, 64 cities that appeared in the first 20 webpage results of a Google search, using 4 keywords, formed the population. A purposive sample of seven destinations (Bangkok, Marrakesh, Mexico City, Istanbul, Hong Kong, Portland and Singapore City) was used. A content analysis method was used to review webpages, journal articles and government reports of the destinations, based on the modified list of CSFs.

The availability of diverse street food resources and cultures, coupled with rich historic city cores that sustain street food vending, in some destinations, are very important CSFs. The presence and extent of regulatory enforcement were also key in others. Empirical research is, however, required to corroborate the draft framework to create a body of knowledge for further research in the field.

The study examines how leading street food destinations have instituted the CSFs required for street food provision.

The COVID-19 pandemic has transformed consumer buying behavior across the world. COVID-19 crisis brought a behavioral change in consumers' attitudes toward health, financial and social well-being. The current research work highlights the factors influencing consumer buying behavior during the COVID-19 pandemic considering saving and safety perspectives.

This study attempts to understand the gap in buying behavior with reference to saving and safety. Survey-based study was conducted during the second phase of COVID-19, and the respondents were those who lived in highly affected COVID cities in India. Exploratory factor analysis and multiple regression analysis were carried out for testing the hypotheses.

Seven factors became the prominent factors in consumer buying patterns during the pandemic. Consumers in the times of COVID-19 pandemic spend only on essential items as compared to nice-to-have and non-essential items.

Respondents considered in the research were millennials aged 25–40. The current research is limited to specific geographic location.

The study assessed how savings and safety influence consumer buying behavior. The 2S framework model for consumer buying behavior during pandemic has been developed. The findings of the study provides a road map to the companies, policy makers, managers and consumers in understanding the consumer buying behavior during pandemic.

The current research work observe the changes in the behavioral patterns of consumers in the context of 2S framework, i.e. saving and safety. This study offer novel contribution as there is no available literature that examined the saving and safety aspects together for consumer buying behavior during crisis.

Successful visionary educational leaders promote a shared vision with great commitment and manage to connect other organizational members to it. In spite of this, the source of their personal commitment to the organizational vision has not yet been the subject of extended study. The purpose of this paper is to correct this by investigating leaders' personal ethos; the personal experiences and values which form their motives and personality. This paper furthermore considers the influence of personal ethos on the content of the vision promoted in educational organizations. Finally, it explores the link between leaders' personal vision and the organizational vision they promote.

Semi‐structured interviews were conducted with visionary educational leaders. These interviews were narrative in nature and aimed to explore the development process and the interrelation of personal and organizational vision in an educational framework.

Data indicate that visionary educational leaders do not separate their personal vision from their organizational vision. Furthermore, the educational leaders interviewed told of formative experiences which affected their worldview and shaped their personal ethos. Personal ethos proved to be a key element in formulating the leaders' personal and organization vision. Four prominent factors emerged as central to the personal ethos of educational leaders: identity, culture and values, professional experience, and family.

The findings suggest that educational leaders should engage in a process of self‐reflection in order to form a significant personal vision to which they can fully commit. Furthermore, the insights of leaders about what is important to them can enable an open dialogue with other organizational members and the development of a shared vision.