Shaha Faisal

The two of factors Orientation towards Finance (ORTOFIN) Scale tests the financial information and personal financial planning of the respondents. The Scale helps in identifying the personal financial management behavior of a general and non-specific nature. The present study was undertaken to test the relationship between status of residence and financial orientation using ORTOFIN Scale. Towards this the ORTOFIN scale was administered on 167 resident employed Indians and 62 expatriates working in Saudi Arabia. Since most of the expatriates work in unique situations that are often beset with risks, they have to face an uncertain future. This unique situation was hypothesized, would induce in them a different type of financial behavior, distinct from those who are settled and work in the home country. Results of the study, however, show that there is no relationship between the status of residence and financial orientation of the respondents. The results of the study are of great significance and of practical implication to those financial institutions with which expatriates are associated.

Employee turnover is a major concern for organizations. Specifically, among private business schools, it is proved to be one of the major impediments in carrying out academic activities. This phenomenon creates a conundrum for both college administrations and students. Therefore, each academic unit must work to minimize employee turnover. This study aims to identify the elements that influence academicians’ turnover intentions and the ways to negate them. It used a random sample of 236 academicians (professors, assistant professors, associate professors, and lecturers) from various business schools in India. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to analyze the sample. In addition, the structural equation modeling (SEM) approach was used to examine the hypotheses. All variables studied had high loadings of 0.50 or more in CFA. The research model was shown to be fit on three important absolute fit indices: absolute, incremental, and parsimonious. The regression weights of hypotheses were also determined to be significant. The findings indicate that organizational support, compensation, and personnel management had a detrimental effect on turnover intentions at business schools. These results can be used by college administration and management in devising interventions that will assist them in retaining existing talented staff and avoiding the negative repercussions of future turnover.

Job embeddedness and person-organization fit are two constructs that reflect employees’ attachment toward their organizations. While job embeddedness enhances employees’ affection, person-organization fit involves the value convergence with the organization. Therefore, the two constructs facilitate continuance in employment. The study attempted to identify the relationship between job embeddedness, person-organization fit, and human capital among the Saudi workforce. Data for the study were collected from 190 full-time employees employed in various organizations in Saudi Arabia. Convenient sampling was used to collect data. The respondents belong to different demographic categories. The data were analyzed using SPSS and structural equation modeling (SEM) techniques. Three hypotheses were formulated for the study to denote the relationships between the variables. For all the hypotheses formulated in the study, t-values (0.71, 0.88, and 0.84) showed a five percent level of significance. The research results thus indicated a significant positive relationship between job embeddedness, person-organization fit, and human capital. The relationship between the constructs and demographic variables was also examined. The findings are significant as the identified constructs help dealing with turnover intention, which is a bane for any organization. Therefore, the results could be used to identify retention strategies.

Acknowledgments
This publication was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia, under the Specialized Research Grant program with Grant No- 2020/02/17318.

The study aims to analyze the factors that influence customers’ inclination to utilize chatbots in banking services. The paper employed the technology acceptance model and utilized structural equation modeling to examine the factors affecting consumers’ willingness to embrace chatbot services. The survey evaluated various determinants, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. Data were collected from 250 bank customers in the Bombay region of India through an online survey employing a random sampling method. The collected data were analyzed using IBM SPSS AMOS. This study identifies the aspects of chatbot technology in the banking sector, such as user interface, content, security, and convenience, that influence customers’ decisions to adopt this innovative technology. The results of the analysis revealed path coefficients indicating a significant relationship between information security and perceived usefulness (β = 0.286; p = 0.005) and between perceived usefulness and intention to use (β = 0.489; p < 0.001). Additionally, the path coefficients for design, security, and facilitating conditions were β = 0.281, β = 0.193, and β = 0.136, respectively, all of which held nearly equal significance in the study. The inter-correlations among the variables ranged from 0.346 to 0.854 and were statistically significant. In the banking sector, customers’ intention to use chatbots is influenced by convenience, efficiency, trust, and personalized experiences. Customers are more likely to embrace chatbots when they provide seamless support and tailored solutions, ultimately enhancing customer satisfaction and engagement.

Acknowledgment
This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445).

The rise in talent management complications led organizations to rely on the latest technologies to automate their routine HRM tasks through AI. This study proposed to examine fundamental aspects of AI in talent acquisition (cost-benefit, opportunities, and risk factors) from the context of strategic analysis and decision-making. 52 respondents from HRM and the information technology departments from fifteen large dairy enterprises, each with more than one thousand employees, were included in the focus group discussion. Both departments were included in the focus group discussion as they heavily employ AI in talent acquisition. The opinions were collected in multiple rounds based on the cost, benefit, opportunity, and risk criteria using the analytical hierarchy process, a multi-criteria decision-making framework. The findings demonstrated that most respondents opinioned AI supports talent acquisition with many opportunities (38.7%) that involve the identification of the best applicants (18.7%) and different benefits (33.2%) to the organization in the form of saving time and cost (16.1%) leading to higher efficacy. The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making. The study recommends that implementing AI in talent acquisition requires a strategic approach, and organizations need to consider factors such as data privacy, ethical use of AI, and ongoing training to ensure successful integration into their hiring processes. Additionally, regular monitoring and adjustments are essential to optimize the effectiveness of AI tools in talent acquisition.

Acknowledgment
The authors of this article would like to thank Prince Sultan University for its financial and academic support for this publication.