The purpose of this paper is to investigate the drivers of firms’ sociability on social media (SM), an unregulated area, in an emerging country, namely, Kingdom of Saudi Arabia (KSA). The sample of this study is 345 non-financial listed firms on Tadawul stock exchange during 2016-2019. Data are collected from several sources such as annual reports, the official website of the sample companies. Other data are collected manually such as the presence of the CEO and the sampled companies on SM. Our findings show that firm size and leverage level are important firms’ characteristics that drive firms’ sociability on social media. The finding shows that CEO sociability on social media is a key CEOs’ characteristic that drives firms’ sociability on social media. Further analysis reports that there is a complementary effect between CEO’s sociability on social media and firm size in increasing firms’ sociability on SM. The findings also show that there is a complementary effect between CEO’s sociability on social media and firm leverage in increasing firms’ sociability on SM. This study contributes to the disclosure literature by providing empirical evidence of the drivers of firms’ sociability on SM, an unregulated area in KSA. It also complements the considerable literature on voluntary disclosure which ignores the use of SM platforms as a “new” voluntary type of reporting. The present study complements recent literature on the adoption of SM by providing evidence that the sociability of top leaders is a driver of firms’ sociability on SM.

This study aims to examine and analyze the influence of social media and Word of mouth on Buying Interest through Brand Image in Creative Economy Business in city of Medan, Indonesia. The population in this study is consumers who visit and buy products at the Creative Economy Business in the city. The number of samples used in this study were 384 respondents with the Cochran formula. The analysis technique used is the Partial Least Squares method, namely, to test the measurement model and structural model. The results show that social media has no significant effect on Buying Interest, Word Of Mouth has a significant effect on Buying Interest, Social Media has no significant effect on Brand Image, Word Of Mouth has a significant effect on Brand Image and Brand Image has a significant effect on Buying Interest. Social Media has no significant effect on Buying Interest through Brand Image as an intervening variable, Word of Mouth has a significant effect on Buying Interest through Brand Image as an intervening variable on Creative Economy Business in Medan City.

Web 2.0 has changed the way consumers access information. This study aims to investigate the relationship between social media (Watsons’ Facebook page) content and consumers repurchase intention. In addition, it determined whether E-WOM and interactivity can act as the mediating variables between the social media content and repurchase decision. The data were collected through online and offline questionnaires. A total of 146 valid questionnaires were obtained and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) through the SMART-PLS 3.3.9 software. The findings support the direct effect of social media content on E-WOM, interactivity, and repurchase intention. Moreover, the results confirmed the mediating role of interactivity between social media content and repurchase intention, however, E-WOM does not mediate between social media content and repurchase intention. The present study suggests some managerial implications for beauty brand retailers and provides fundamental strategies related to their social media.

Web 2.0 has changed the way consumers access information. This study aims to investigate the relationship between social media (Watsons’ Facebook page) content and consumers repurchase intention. In addition, it determined whether E-WOM and interactivity can act as the mediating variables between the social media content and repurchase decision. The data were collected through online and offline questionnaires. A total of 146 valid questionnaires were obtained and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) through the SMART-PLS 3.3.9 software. The findings support the direct effect of social media content on E-WOM, interactivity, and repurchase intention. Moreover, the results confirmed the mediating role of interactivity between social media content and repurchase intention, however, E-WOM does not mediate between social media content and repurchase intention. The present study suggests some managerial implications for beauty brand retailers and provides fundamental strategies related to their social media.

Word-of-mouth (WOM) has been recognized as one of the most influential forms of information transmission. Advances in information technology and the emergence of online social networking sites have changed the manner in which data is delivered. This phenomenon has an impact on consumers because readily accessible information can significantly influence consumption-related decisions. The purpose of this paper is to examine the role of e-WOM in mediating the influence of the marketing mix and destination image on the intention of tourists to re-visit destinations. The primary data was derived from 190 tourist respondents and collected by means of questionnaires distributed via Google forms. The resulting data was analyzed using AMOS-SEM, while evaluating the role of e-WOM as a model involving a Sobel test. The results of the analysis indicated that e-WOM plays a highly significant role in piquing individual's' interest in re-visiting tourist sites. Contributory factors such as the marketing mix, destination image, and e-WOM all support this research hypothesis.

This study aims at investigating the effect of digital HRM practices on employee motivation and hence employee job performance, or in other words, the mediating role of employee motivation between digital HRM practices and employee job performance. Two digital HRM practices were used in this study: digital training and digital performance appraisal. Collecting data using a valid and reliable questionnaire from employees at industrial companies, the results show that digital training had significant effects on both employee motivation and job performance, digital performance appraisal had significant effects on employee motivation and performance appraisal, and employee motivation exerted a significant effect on job performance. Consequently, it was approved that employee motivation partially mediated the effect of digital HRM practices on job performance. It was concluded that skilled employees who are aware of their performance level are motivated to show higher levels of job performance. Theoretically, the study called scholars to carry out further results to examine the effects of other HRM practices on job performance through employee motivation. Empirically, organizations are requested to conduct training sessions and assess employee performance using digital means.

In recent years, technology adoption in Indonesia has begun to use advances in artificial intelligence (AI) to improve services. This change has had a significant impact on several institutions. This article provides an overview of the many institutions in Indonesia that are leveraging AI. A comprehensive review of 35 papers from the Scopus database was used to develop our methodology. Education, health care, ICT, licensing, transportation, and economic services are all well-represented in the existing literature, which puts AI into practice in various disciplines. We provide a framework for organizing governance research that identifies gaps in the existing literature and suggests future directions for research utilizing technology.

The Internet of Things (IoT) is an innovative technology that has the potential to help public hospitals better meet the demands of hospitalization. However, only a small portion of the research looked at patients' behavioural intentions (BI) to utilise IoT healthcare devices (IoTHD). This study intends to investigate the variables that influence the BI's use of IoTHD. The research suggests that the BI may be explained by factors of UTATU. The patients of public hospitals make up the population. A questionnaire was used to obtain the data using convenience sampling. Participants in this research totalled 161. Smart Partial Least Square results demonstrated that social influence (SI) has an impact on performance expectancy (PE). Technological complexity (TC) and playfulness (PP) had an impact on effort expectancy (EE). Additionally, the BI to adopt IoTHD was impacted by PE, EE, perceived security (PS), and perceived privacy (PV). The impact of PE and EE on BI to use IoTHD was not moderated by the facilitating conditions (FC). In order to improve patients' perceptions of IoTHD usage in public health organisations, simple process and more positive word of mouth is required.

This research studies the effects of the religiosity on financial technology (fintech) adoption. The study examines religiosity as part of the Technology Acceptance Model (TAM) dimensions for the adoption of mobile payment technology. We explore the role of religiosity in TAM and recommend several policies for related organizations. The study uses professional sample calculation from 113 traditional markets under Perumda Pasar Jaya as a business entity whose capital is wholly or mostly owned by the regional government through regional assets of DKI Jakarta Province, Indonesia, which use mobile payment technology. We obtained 363 respondents from June 2020 to June 2021, coinciding with the Covid-19 pandemic. Hypothesis testing was done employing SmartPLS 3.2.9 software and questionnaires. The study also adapts previous studies to ensure the questionnaires are relevant to the research objects. The research result show that religiosity explained the formation of TAM by small businesses in traditional markets under Perumda Pasar Jaya Management. Religiosity and the adoption of mobile payment technology determined whether a user used fintech or not. As the research period was limited to June 2020 - June 2021, including field research in the traditional markets, newer TAM mobile payment technology development and other TAM mobile payment-based research were not included. This research offers a new TAM development model using religiosity for mobile payment adoption in traditional markets.

Phishing is an attack by cybercriminals to obtain sensitive information such as account IDs, usernames, and passwords through the use of the anonymous structure of the Internet. Although software companies are launching new anti-phishing tools that use blacklists, heuristics, visual methods, and machine learning-based methods, these products cannot prevent all phishing attacks. This research offers an opportunity to increase accuracy in the detection of phishing sites. This study develops a model using machine learning algorithms, specifically the decision tree and the random forest, due to their outperforming the rest of the classifiers and being accredited by researchers in this field to achieve the highest accuracy. The study is based on two phases: the first phase is to measure the accuracy of classifiers on the dataset in the usual way before and after feature selection. The second phase uses the class decomposition approach and measures the accuracy of classifiers in the dataset before feature selection and after feature selection to detect phishing sites. The class decomposition approach is a technique to improve the performance of classifiers by distributing each class into clusters and renaming the examples of each cluster with a new class. This provides a specific metric that more accurately predicts the level of phishing. Testing on a dataset containing 11,055 instances, 4,898 phishing, and 6,157 legitimate, each instance has 30 features. It achieved the highest accuracy in the first phase through the random forest algorithm by 96.9% before feature selection, and after feature selection, it was by 97.1%. In the second phase, the highest accuracy of both the decision tree and random forest classifiers was achieved by 100% with the two and four classes after feature selection. While before feature selection, the random forest algorithm achieved 100% with only the two classes.