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This paper is a tribute to researchers who have significantly contributed to improving and advancing structural equation modeling (SEM). It is, therefore, a brief overview of SEM and presents its beginnings, historical development, its usefulness in the social sciences and the statistical and philosophical (theoretical) controversies which have often appeared in the literature pertaining to SEM. Having described the essence of SEM in the context of causal analysis, the author discusses the years of the development of structural modeling as the consequence of many researchers’ systematically growing needs (in particular in the social sciences) who strove to effectively understand the structure and interactions of latent phenomena. The early beginnings of SEM models were related to the work of Spearman and Wright, and to that of other prominent researchers who contributed to SEM development. The importance and predominance of theoretical assumptions over technical issues for the successful construction of SEM models are also described. Then, controversies regarding the use of SEM in the social sciences are presented. Finally, the opportunities and threats of this type of analytical strategy as well as selected areas of SEM applications in the social sciences are discussed.

With the increasing popularity of social media, a new ethics debate has arisen over marketing and technology in the current digital era. People are using online communities but they have concern about information credibility through word of mouth in these platforms. Social media is becoming increasingly influential in shaping individuals' decision-making as more and better quality information about products is made available. In this research, a social word-of-mouth model proposes using a survey to test the model in a popular travel community. The model highlights the role of social media and social support in social networking sites (SNSs), identifying increasing credibility and information usefulness resulting in an ethical environment to adopt word of mouth. The theoretical and practical implications of the study are both detailed.

The strength model of self-regulation uses a muscle analogy to explain patterns of ego depletion, conservation of willpower, and improved performance after frequent exercise. Our 2007 overview of the literature has been well cited, presumably because of the phenomenon’s importance to theories of selfhood and a wide assortment of applied contexts, including problem behaviors. Some researchers have put forward rival theoretical accounts, and others have questioned the existence of the phenomenon. The weight of evidence continues to support the usefulness of the strength model, albeit amid continuing updates and revisions.

Indonesia has low e-commerce transactions despite high internet usage. This study examines the e-repurchase intention on Lazada Indonesia, an e-marketplace with declining traffic and sales. This study uses the perceived usefulness of institutional-based mechanisms, the perceived usefulness of seller-based mechanisms, and the perceived usefulness of experience-based mechanisms to examine how trust in the e-market and e-seller affect repurchase intention. This quantitative study includes 231 Lazada Indonesia customers from the past three months (the survey was conducted in January 2023). The data were statistically analyzed with partial least squares structural equation modeling (PLS-SEM). 43.72% of the respondents shop one to three times a month, 42.42% – more than three times per month, and 13.85 – less than once per month. Trust in the e-marketplace increased when participants believed institutional-based processes were beneficial (with a beta value of 0.272 and a P value of 0.000) and seller-based mechanisms were valuable (with a beta value of 0.509 and a P value of 0.000). In terms of trust in the e-seller, only the perceived usefulness of seller-based mechanisms has a significant effect (with a beta value of 0.567 and a P value of 0.000), while the perceived usefulness of experience-based mechanisms has no effect. This study has also shown that e-seller trust significantly affects repurchase intention. Finally, with a beta value of -0.055 and a P value of 0.046, e-marketplace trust negatively moderates the relationship between e-seller trust and repurchase intention. Thus, e-marketplace trust can replace e-seller trust in customer repurchase intentions.

E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.

Purpose The paper aims to understand the possible determinants of knowledge of, and interest in using, blockchain, with a particular focus in the future intention to apply this technology. Blockchain technology is deemed to radically change business models and processes. Using this technology in small and medium enterprises (SMEs) is still a novel idea. Moreover, not much is known about the diffusion and level of interest towards blockchain in SMEs. This research adopts a knowledge management perspective, drawing on technology acceptance model to highlight the level of blockchain technology diffusion, and to explore which factors lead SMEs’ to adopt blockchain. Design/methodology/approach This study distributed a questionnaire to a sample of 300 SMEs in Italy. This study received 96 responses (32% response rate). This study calculated descriptive statistics and undertook a reliability analysis. Finally, this study performed a logistic regression to analyse the determinants of further intention to use blockchain technology. Findings Results show that blockchain technology is quite well known, but the level of knowledge is limited. Moreover, the research reveals that the rate of adoption is very low. Interest in the future adoption of blockchain is associated with knowledge, perception of usefulness and ease of use of blockchain. Originality/value This paper is one of the first explorative studies showing which factors lead SMEs to adopt blockchain technologies and shedding some light on the interaction between knowledge management and blockchain adoption and diffusion in SMEs. It highlights how blockchain knowledge could determine future interest in blockchain innovation. This paper is relevant for public and private institutions that aim to promote, through knowledge management, the adoption of blockchain in SMEs.

Purpose – The aim of this study is to shed light on the factors influencing continued use, attitude, and satisfaction with gamified mobile app usage. A research model is proposed, featuring achievement, social, confirmation, ease of use, enjoyment, recognition, and social influence as independent variables. Dependent variables encompass continued use, attitude, satisfaction, motivation, and usefulness. Design/methodology/approach – Data were collected from 1,633 respondents who use gamified shopping apps. Hypothesis testing was conducted using path analysis, and the Bootstrapping method was used to test the significance level of each relationship. Findings – Results indicate that achievement and social factors have a positive impact on motivation. Motivation, confirmation, and usefulness all positively influence satisfaction. Usefulness is shaped by confirmation and ease of use. Moreover, usefulness, ease of use, and social influence positively correlate with attitude and continued use. Contrarily, neither enjoyment nor recognition appeared to influence attitude or continued use. Research limitations/implications – This study did not incorporate frequency and personal traits into the model. While the results are particularly relevant to shopping apps, their applicability may extend beyond this context. Future research could consider frequency and personal characteristics as moderating variables and sample objects from varied industries. Practical implications – When developing gamification strategies, businesses ought to take into account both utilitarian and social aspects of gamification. It's vital to understand user expectations, prioritize user-friendly gamification interfaces, and promote positive word-of-mouth. Originality/value – This research enriches both the gamification and marketing literature by introducing a model grounded in gamification elements, human motivation theory, and the expectation-confirmation paradigm. It underscores the pivotal role of utilitarian and social facets in shaping usefulness, motivation, satisfaction, attitude, and continued app use. Notably, this work paves the way for further exploration into the roles of enjoyment and recognition.

We examined the relationships between the determinants that affect customers' use of food delivery apps. Using an extended technology acceptance model, we explored consumers' experiences in purchasing delivery food through mobile apps. We distributed a self-administered questionnaire online and used structural equation modeling to test the hypotheses. We found that user-generated information, firm-generated information, and system quality had a significant effect on perceived usefulness. In addition, system quality and design quality strongly influenced the perceived ease of use, which improved perceived usefulness, and in turn, perceived usefulness and perceived ease of use affected attitude toward the use of mobile apps. Practical implications for the food service industry are discussed.

In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term \"Google Trends\" in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Prediction of suicidal behaviour is an aspirational goal for clinicians and policy makers; with patients classified as 'high risk' to be preferentially allocated treatment. Clinical usefulness requires an adequate positive predictive value (PPV). To identify studies of predictive instruments and to calculate PPV estimates for suicidal behaviours. A systematic review identified studies of predictive instruments. A series of meta-analyses produced pooled estimates of PPV for suicidal behaviours. For all scales combined, the pooled PPVs were: suicide 5.5% (95% CI 3.9-7.9%), self-harm 26.3% (95% CI 21.8-31.3%) and self-harm plus suicide 35.9% (95% CI 25.8-47.4%). Subanalyses on self-harm found pooled PPVs of 16.1% (95% CI 11.3-22.3%) for high-quality studies, 32.5% (95% CI 26.1-39.6%) for hospital-treated self-harm and 26.8% (95% CI 19.5-35.6%) for psychiatric in-patients. No 'high-risk' classification was clinically useful. Prevalence imposes a ceiling on PPV. Treatment should reduce exposure to modifiable risk factors and offer effective interventions for selected subpopulations and unselected clinical populations.