Search results

Construction projects in the real estate sector are important for a nation’s economic growth. However, owing to several constraints and stereotypes, the construction industry is suffering from major capital losses. A review of recent literature revealed that risks occurring in real estate construction projects (RECPs) carried out in developing economies can be divided into four categories, i.e. financial risks, design risks, execution risks and sociopolitical risks. This study aims to identify the critical factors that lead to these risk categories and develop a control structure for RECPs.

A quantitative method, i.e. a questionnaire-based survey, was used for this study. Using a random sampling technique the questionnaire was distributed to selected, highly experienced project managers. To determine whether the factors identified as part of the literature review strongly influence the categories, the factor analysis of the observations was performed.

The observations made in this study lead to the identification of six critical risk causing factors, i.e. lack of efficient planning, execution constraints, external constraints, client-induced constraints, project constraints and partner experience. The observations indicated a lack of knowledge of project management, organizational culture and a claims redressal mechanism in RECPs. Also, this study recommends a blockchain-based control structure to control the occurrence of the risk causing factors identified.

This study recommends a blockchain-based control structure for controlling the risks occurring.

This paper aims to discuss innovations in the training and development practices of companies and delineate a new approach to training and development in the context of the hybrid workplace using the ADDIE and Kirkpatrick training models.

This paper discusses innovations in training and development in modern times and builds on the instructional training design approach or the ADDIE Model and the Kirkpatrick Model of training evaluation.

The paper presents new approaches to training and development in the context of the hybrid work model applying the ADDIE Model and the Kirkpatrick Model. These new approaches are both necessitated and also made possible due to the technological advancements of modern times.

With the rapid transition of companies to the hybrid model of work in recent times, several human resource management practices need to be transformed to suit the requirements of the new work model. Training and development is one function that needs to change in the hybrid work model to ensure its effectiveness. This paper analyses innovations in the training and development practices of companies and discusses new approaches while applying existing training models, the ADDIE and Kirkpatrick Models, to adapt to the changes associated with the hybrid work model.

In response to scholarly calls, this study aims to explore the persuasive predictors including (job security, autonomy, work-life balance, culture and climate, career growth, leadership style and support, training and development and reward, recognition and pay) of talent retention in Indian PSUs by emphasising the commonalities and differences between different category of employees from different cohorts based on their contribution in the prime function of the business.

A self-administered questionnaire was used to collect data from 472 employees from selected organisations through Multistage sampling technique and Simple random sampling technique. Data were analysed using tests such as logistic regression, MANOVA, post hoc Tukey in SPSS 20.

Job security, autonomy, career growth and culture and climate are the persuasive predictors of talent retention in Indian PSUs. However, there is a significant interaction effect of age and job standard on job security. Job security and perceived employability do not relate to age for core category employees rather possess identical perception than other employee category. Findings indicate that identifying the position to be retained is more viable than a common retention strategy.

This study contributes to the existing knowledge of talent retention by exploring novel insights in comprehending the commonalities and differences of different employee category at all level from different cohorts, sharing different responsibilities in the prime function of the business that has largely been ignored. The study can succour in improving performance and cost-effective retention strategy.

The purpose of this paper is to evaluate the pricing implication of aggregate market wide investor sentiment risk for cross sectional return variation in the presence of other market wide risk factors.

The paper employs the Fama and French time series regression approach to examine the impact of market risk premium, size, book‐to‐market equity, momentum and liquidity as risk factors on stock return. Given the importance of inherent imperfect rationality or sentiment risk, the paper further investigates the impact of investor sentiment on the cross section of stock return.

The choice of a five factor model is apparently persuasive for consideration in investment decisions. Stocks are hard to value and difficult to arbitrage with characteristics which are significantly influenced with the sentiment risk. It is naïve to argue for the universal pricing implication of sentiment risk in a multifactor model framework.

The test assets portfolios are not segregated as per any industry criteria.

Investment managers can use a contrarian investment strategy, for the stocks that are hard to value and riskier to arbitrage to gain excess return when the market follows a downward trend.

This makes the first attempt towards the investigation of the impact of the sentiment risk on cross sectional return variation from an emerging market perspective on such a diversified and large test asset portfolios. The paper has extended the available literature by investigating the impact of sentiment risk after controlling the liquidity risk factor in a multifactor specification. This measure of market wide irrational sentiment index is more comprehensive.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

The purpose of this review paper is to address the need for mapping the thematic structure of fuzzy-set qualitative comparative analysis (fsQCA) in the context of tourism and hospitality research, in response to the growing interest in this methodology.

This study conducts a comprehensive systematic review using the SPAR-4-SLR procedure to produce a scientific map of top-tier research using the fsQCA technique in tourism and hospitality studies.

A major finding of this review investigation is the identification of six knowledge clusters: business competitiveness in the hospitality sector; psychoanalysis of tourist behaviour and satisfaction; entrepreneurship in the tourism and hospitality sector; tourism business ecosystem; sustainable tourism; and service quality in the tourism and hospitality sectors.

Although this review assessment relied heavily on articles published in prestigious journals, it does not rule out the idea that other sources, such as conference proceedings, can provide valuable insight.

Novel insights into the intellectual structure of tourism and hospitality research using the fsQCA technique, as well as ideas for future exploration in this domain, are presented in this review.

The purpose of this paper is to develop, test, and validate a model in a specialty retail environment to assess the influence of a salesperson’s sales- or customer-orientation and customer characteristics related to buy/no-buy decisions.

Backward stepwise discriminant analysis was used to identify variables that most differentiated buyers from non-buyers. The discriminant model was estimated with survey data provided by a judgment sample of consumers asked to recall details about a recent in-store purchase experience (n=240). One significant discriminant function emerged. The model correctly classified 87.5 percent of buy/no-buy decisions by consumers in a separate validation sample (n=40).

Customers who believe a salesperson is sales oriented (i.e. only interested in closing) are more likely to make a no-buy decision even when retailer-related attributes – such as positive prior experience with the retailer, susceptibility to normative interpersonal influence, and positive attitude toward retailing – suggest otherwise. Surprisingly, neither customer orientation nor susceptibility to interpersonal informational influence relates significantly to making a buy/no-buy decision.

Specialty retailers should avoid a sales-outcome-based orientation. To add value in a competitive marketplace where buyers can avoid salespeople, the focus of a sales interaction should be on identifying customer needs and characteristics.

Adaptations of sales people’s personas and selling efforts – fostered by new managerial training practices – and the need for specialty retailers to adopt behavior-based control systems are suggested. In addition, sales or customer orientation typically is reported by the salesperson. Here, customers’ belief – which is more germane to modeling buy/no-buy decisions – designates the salesperson’s orientation.

The purpose of the study is to confirm the fact that in informations security, the human factor was considered as a key carrier of the majority of attacks that an information system faces. Banking and other financial services are always top among the most attractive targets for cyber attackers. Blind phishing or spear phishing is still one of the major contributors to all malicious activities in the e-banking sector. All the counter mechanisms, therefore, revolve around the concept of how security-aware the customers are. To fool these mechanisms, attacks are becoming smarter and are searching for methods where the human involvement is diminishing to zero. Zero click attacks are one big leap that attackers are taking that removes the requirement of human involvement in initiating attacks and are moving toward an era of unassisted attacks. Even though the standard procedure and protocols are built into the banking system, they fail to detect this attack resulting in significant losses.

This paper follows a conceptual review of the upcoming concept in security and its implication in e-banking sector. The methodology adopted in this paper uses review papers, articles and white papers to conclude a theoretical model. A detailed analysis of unassisted attacks is considered from 2010 onwards till 2022.

This research deliberates on the methodologies of zero click attacks and gives a detailed analysis of attack vectors and their exploits. This research also identifies the likely attacks on e-banking that these vulnerabilities can trigger.

The key contribution is toward the early detection of zero click attacks, suggesting countermeasure, reducing the likelihood of these attacks and the financial impact.

The purpose of this paper is to investigate the relationship between dividend policy and the life cycle of firms in India. In addition, this study intends to examine the variation in dividend behaviour over the life cycle of a firm. The study anticipates that a firm's dividend behaviour varies over its life cycle.

To scrutinize the validity of the proposition, the authors classify 1968 non-financial industrial firms listed at Bombay Stock Exchange (BSE) into growth, mature and stagnant firms over the period 2000–20. Additionally, to check the robustness of the results, they use an array of techniques such as analysis of variance, pooled ordinary least squares, fixed effects models and random effects models.

The empirical findings suggest that dividend behaviour varies over a firm's life cycle. Specifically, stagnant firms are paying significantly higher dividends than growth firms. Mature firms are paying significantly higher dividends than growth firms. The results are consistent after controlling the effects of firm's size, profitability, leverage, operating risk, systematic risk and growth opportunities.

The findings are useful for corporate decision makers in establishing an appropriate dividend policy conditional on firms' life cycle stage and for shareholders in making investment decisions.

The relation between dividend policy and firm life cycle has not been examined before in the context of Indian stock market. Thus, this research bridges this gap in the literature.

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe.

This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness.

The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers.

Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse.

This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.