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Understanding the effect of assortment composition on attentional levels is an essential topic for academic researchers and practitioners. This work has important implications when analyzing the influence of shopping frame time and search effort on the relationship between the reaction to assortment composition and visual attention to stock-keeping units (SKUs) pricing.

Two experimental studies through gauze behavior analysis technology (using eye-tracking equipment) analyze the variable's large assortment, visual attention to SKU pricing, search effort and shopping frame time.

The results suggest that, although it increases the search effort, a large assortment decreases the visual attention to SKU pricing. Further, our results indicate a moderating effect associated with mitigating the negative effect by medium-low levels of search effort and a moderating impact of time in this relation.

Marketing professionals can carefully optimize the in-store experience by managing the assortment and variety and by influencing consumers' visual attention to SKU pricing along the journey as part of the experience. Assortment and SKU pricing strategies need to be aligned with consumer journey design.

Our findings contribute to assortment theory and management by detailing the relationship between consumers' reactions to assortment perception and visual attention to SKU pricing in time flow. We reinforce the importance of considering assortment strategies from the consumer perspective and giving reliable information about in-store behavior.

Job satisfaction is essential in understanding turnover intentions. Previous studies reveal that highly educated hospitality employees generally have lower levels of job satisfaction, indicating that the antecedents of job satisfaction may be different from hospitality managers and frontline employees. This study compared the different antecedents of job satisfaction for housekeeping managers and employees.

This study used a mixed-methods approach for a two-part study. The researchers recruited housekeeping managers for the exploratory survey. The results of open-end questions helped us build a custom dictionary for the text mining of comments from Glassdoor.com. Finally, a multilinear regression of themes from housekeeping employees’ ratings on Glassdoor.com was conducted to understand the antecedents of job satisfaction for housekeeping managers and employees.

The results of the exploratory survey indicated that the housekeeping department has an urgent need for organizational support and training. The text-mining revealed organizational support impacts both managers and frontline employees, while training impacts managers more than employees. Finally, the regression analysis showed compensation, business outlook, senior management, and career opportunity impacted both groups. However, work-life balance only influenced managers.

With a large number of employees at low salaries, housekeeping departments have a higher-than-average turnover rate for lodging. This study is among the first to compare the antecedents of managers’ and frontline employees’ job satisfaction in the housekeeping department, extending Social Exchange Theory. It provides suggestions for the housekeeping department to decrease turnover intentions.

This study is to examine and develop sector skills strategies and action plans for the textile and apparel (T&A) sector.

The paper used a participatory action qualitative method anchored on the Skills for Trade and Economic Diversification (STED) framework, utilising the workshop-based approach with 24 key stakeholders of the sector. Content analysis was used with the help of Nvivo software.

The findings revealed that there are skills shortages, skills gaps, skills mismatches and skills diversification programmes available through higher education and work-based learning. Further, there are labour supply challenges such as national skills policy and strategy, government and stakeholder coordination, funding, relevance of curriculum and qualifications, access to practicals and the absence of a clear national vision for the sector.

This study possesses an inherent limitation in terms of generalising the findings derived from qualitative research.

This research is among the first of its kind to assess skills needs and gaps through the lens of STED framework, which has been overlooked in previous literature. Importantly, this study provides vocational insights into skill needs in the sector.

Over the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.

The study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.

This study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.

This work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.

High turnover of project managers is a common phenomenon in the construction industry, which has a negative impact on the productivity and performance of construction firms. The study investigates the mechanisms of person-environment fit on turnover intention of construction project managers and the mediating role of job embeddedness. The authors also tested the moderating role of perceived organizational support in the influence of job embeddedness on turnover intention.

The data were collected from managers of 62 construction and infrastructure projects in Wuhan. Based on person-environment fit theory, job embeddedness theory and social exchange theory (SET), the authors employ structural equation modeling (SEM) to examine the hypotheses.

Results show that if project managers are not well-fitted with the environment of organizations, it reduces their embeddedness in jobs, which in consequence makes them more inclined to leave. Job embeddedness mediates the relationship between person-environment fit and turnover intention. In addition, the authors validated the moderating effect of perceived organizational support, showing that the higher the employee's job embeddedness, the lower the employee's turnover intention.

Construction companies can retain project managers and stabilize management teams through effective management strategies, thus effectively reducing the separation costs of construction companies.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The primary purpose of this study was to examine the underlying consumer values that drive hotel booking mobile app users to engage more with the app and use the app continuously for hotel booking purposes.

By conducting confirmatory factor analysis (CFA) and structural equation modeling (SEM) on the data collected through the Qualtrics online survey platform from 506 respondents in the United States, the proposed measurements and structural models were tested.

The findings for both Generation Xers and Millennials revealed that hedonic and social values influenced mobile app engagement, which strongly influenced mobile app loyalty. Thus, to enhance customer engagement and indirectly influence mobile app loyalty, the app value delivery should appeal to social and hedonic values. Although the utilitarian values for using apps had a potential direct impact on mobile app loyalty, the mediation analysis showed that mobile app engagement, when connecting consumption values to mobile app loyalty, served as a full mediator for Generation Xers and a partial mediator for Millennials.

This study provides insights into how hotels and online travel agent (OTA) marketing managers may consider augmenting user engagement with hotel booking mobile apps. This study suggests that hoteliers and OTAs should further develop their user experience efforts to enhance the utilitarian features of their mobile app to increase revenue from repeat purchases. Additionally, this study provides implications for enhancing the hedonic and social features of hotel booking mobile apps to appeal to Millennials and Gen Xers.

This study developed and assessed an integrated model to investigate the relationships between consumption values, engagement and loyalty in hotel booking mobile apps. Furthermore, it examined generational cohorts' role in the relationships between these constructs.