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Enterprise resource planning systems (ERPs) have provided new challenges in the management of organizations’ internal and external risks, and their adoption has triggered groundbreaking changes to internal audit practices. This study aims to shed light on the use of ERPs in internal auditing by identifying interrelations between postevaluations of the ERPs’ quality dimensions with internal auditors’ satisfaction, intentions to continue using such systems and perceived benefits.

Drawing on a unique data set of internal auditors’ responses on a structured questionnaire, and by using the DeLone and McLean’s (2003) Information Systems success model as the conceptual framework, this study tests the research propositions by using partial least square structural equation modeling (PLS-SEM).

The findings report statistically significant positive relationships among all three ERPs’ quality dimensions (system, information and service quality) with internal auditors’ satisfaction and intention to continue using these systems. Moreover, the study found that the benefits perceived by internal auditors were significantly influenced by their satisfaction with the system and their intention to continue using it.

The authors survey ERP postevaluation success factors in two unique contexts: internal auditors and Greece. Thus, the authors ground on previous research findings in diverse professional groups and national environments. In parallel, this study lends conceptual clarity and empirical evidence to a small but growing number of studies examining the implications of individuals’ perceptions, intentions and behavioral reactions in the context of ERP implementation.

Even though the end-user satisfaction construct has gained prominence as a surrogate measure of information systems performance assessment, it has received scant formal treatment and empirical examination in the data analytics systems field. In this respect, this study aims to examine the vital role of user satisfaction as a proxy measure of data analytics system performance in the financial engineering context.

This study empirically validated the proposed model using primary quantitative data obtained from financial managers, engineers and analysts who are working at Jordanian financial institutions. The quantitative data were tested using partial least squares-based structural equation modeling.

The quantitative data analysis results identified that technology quality, information quality, knowledge quality and decision quality are key factors that enhance user satisfaction in a data analytics environment with an explained variance of around 69%.

This empirical research has contributed to the discourse regarding the pivotal role of user satisfaction in data analytics performance in the financial engineering context of developing countries such as Jordan, which lays a firm foundation for future research.

Turn-taking is beneficial to conversational search success, but the increase in turns and time can also increase the cognitive load of the user. Therefore, in this research paper, the authors view conversational search sessions through the lens of economic theory and use the economic models of search to analyze the various costs and benefits of information-seeking interactions.

First, the authors built a cost-benefit model for conversational search sessions by defining action types and performing an intellectual mapping of actual sessions into sequences of these actions (using thematic analyses). The authors used the hypothesized cost and benefit actions (obtained from the user-system dialogs), along with the number of turns, utterances and time-related parameters, to propose the mathematical model. Next, the authors tested the model empirically by comparing the model scores to the user satisfaction and task success scores (collected through questionnaires). By representing each session as a bag of actions, the authors developed linear regression models to predict task success and user satisfaction.

Through feature analysis and significance testing, the authors identify the different parameters that contribute significantly to user satisfaction and task success scores. Error analysis shows that the model predicts task success and user satisfaction reasonably well, with the average prediction error being 0.5 for both (on a 5-point scale).

The authors' research is an initial step toward building a mathematical model for predicting user satisfaction and task success in conversational search sessions.

This study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information support, service quality, perceived intelligence, goal-congruent outcome and self-efficacy.

The study utilized a survey of office workers, analyzed through structural equation modeling, to explore these determinants.

The results demonstrate that system quality, service quality and perceived intelligence significantly influence satisfaction, while service quality and perceived intelligence also impact goal-congruent outcomes. Contrary to traditional models, satisfaction does not significantly correlate with usage. Instead, a significant relationship is observed between goal-congruent outcomes and usage. Self-efficacy emerges as a crucial predictor of subscription intentions, further underlined by the significant impact of usage on subscription intention.

The study’s focus on office workers and a single artificial intelligence (AI) chatbot type may limit generalizability. Its findings illuminate several avenues for future research, particularly in diversifying the context and demographics studied.

This research offers actionable insights for businesses and practitioners in the implementation of AI chatbots. It highlights the importance of enhancing system quality, personalization and user confidence to boost subscription intentions, thereby guiding strategies for user engagement and technology adoption.

This study pioneers in investigating subscription intentions towards AI chatbots, particularly ChatGPT, providing a novel framework that expands upon traditional user behavior theories.

The purpose of this study was to empirically investigate the role of e-service quality factors in predicting e-satisfaction. The study context was spirituality and well-being over-the-top services. The e-service quality factors consisted of perceived functional completeness, perceived performance, perceived quality of interface and interaction, perceived quality of content and information and perceived quality of customer support. The study goal was to ascertain over-the-top services customers’ behavioral intention toward upgrading to premium subscription and the spread of electronic word of mouth.

This study was based upon the integrated stimulus-organism-response framework where e-service quality represented the stimulus, e-satisfaction the organism, behavioral intention and electronic word of mouth as the response. The study used a moderated-mediation approach with e-satisfaction as the mediator and the price value of a premium subscription as the moderator. To empirically test the model, the authors collected data from 312 spirituality and well-being over-the-top services users in India. Partial least squares-structured equation modeling was used to analyze the collected data.

The findings of the study supported the association between e-service quality factors and e-satisfaction while using spirituality and well-being over-the-top service. The results furthermore indicated that satisfied spirituality and well-being over-the-top customers were willing to upgrade to the premium subscription and spread favorable electronic word of mouth. The moderated-mediation study results revealed that the price value of premium subscriptions moderated the relationship between e-service quality and e-satisfaction but did not moderate the relationship between e-satisfaction and behavioral intention, and e-satisfaction and electronic word of mouth.

This study offered a comprehensive stimulus-organism-response theoretical model by using the five e-service quality measurement factors as “stimuli” for motivating the internal state of spirituality and well-being over-the-top subscribers. This was toward sustained usage in over-the-top services subsequent to the end of the freemium period. Furthermore, in this study, both e-service quality theory and user satisfaction theory were integrated into the stimulus-organism-response model. This helped to better comprehend the impact of e-service quality factors in driving e-satisfaction among spirituality and well-being over-the-top service users.

This study revealed the significance of differentiating premium over-the-top subscriptions based on price value. To ensure a high level of e-satisfaction from a premium subscription, a greater emphasis on the e-service quality dimensions was required. This study provided insights to managers regarding the role of favorable electronic word of mouth in fostering effective customer acquisition.

This was one of the first studies which concurrently integrated perceived value of the premium subscription and e-satisfaction with customers’ behavioral intention and electronic word of mouth through the theoretical lens of stimulus-organism-response.

Advances in autonomous technology have transformed customer experience. Most prior research has investigated the effect of augmented reality (AR) on purchase intention, yet few research has discussed the effect of semiautonomous AR in the context of service use. Semiautonomous AR recognizes content in the present reality, inserts and adjusts virtual content, supervises the users and enables them to feel in control of the virtual content overlaid in observed reality resulting in enriched user experience and thereby augmentation experience. This research demonstrates how perceived control of virtual content leads to higher perceived augmentation experiences among semiautonomous AR users than among non-AR users. In addition, this research examines the mediation effects of enriched user experience and perceived augmentation experience on user satisfaction and users’ willingness to continue using AR. Results also indicate that AR users perceive a higher augmentation experience than non-AR users. However, users’ willingness to continue using AR is not significantly different between AR and non-AR users.

This study derives six hypotheses and uses a preliminary study, a field study and a lab study to evaluate the hypotheses. A field study was conducted in a car dealership to test the hypotheses, and a lab experiment was conducted in a controlled setting to corroborate the results obtained in the field study and test the underlying causal effects.

Semiautonomous AR can constantly sense, plan and not necessarily always act over the virtual content to sustain the interaction with its users. Perceived control of virtual content enhances perceived augmentation experience, and its effect of perceived control of virtual content on perceived augmentation experience is higher among semiautonomous AR users than among non-AR users. Perceived control of virtual content is a key to enriched user experience, augmentation experience and thereby users’ attitude and behavior. In addition, results showed that enriched user experience mediates the effect of perceived control of virtual content on perceived augmentation. User satisfaction mediates the effect of perceived augmentation experience on users’ willingness to continue using AR. The theoretical and practical contributions are comprehensively discussed.

Some limitations of the studies are ascertained. First, a larger sample size might be required to achieve generalizability and a strong test of the applied theory. Second, new field studies can reflect customers’ real attitudes and behaviors so as to reveal realistic interactions between the device properties and the human will in solving actual problems. The user is interested in participating in the solution within the sensing-planning-acting process as depicted by this research. Third, new research to test AR’s capabilities in bounded and symbiotic conditions can illustrate the level of autonomy each type requires, providing additional insights into why supervised AR autonomy best reflects semiautonomous AR. The pioneering structural model offered in this study (perceived control of virtual content-perceived augmentation experience-users’ satisfaction-users’ willingness to continue using AR) should be tested with new samples in other industries, aside from including other variables that may enrich the model and increase its explanatory power. In addition, future research might use other AR devices such as smart glasses to explore the effects of AR on perceived control of virtual content, enriched user experience and perceived augmentation experience. Future studies can investigate the effect of auditory and visual augmentation on enriched user experience and perceived augmentation experience, and involve features of artificial intelligence (AI) to assist users in decision-making. Regarding context, this research showed that age and gender differences did not affect the results. Nonetheless, age and gender, and perhaps additional demographic characteristics, may concern future studies.

Some recommendations for technology developers are derived from this research. AR is revolutionizing service experience. As technologies are becoming autonomous, developers seek ways to design experiences to enhance consumers’ sense of control over their interaction with such systems. Companies cannot create customer experience (Becker and Jaakkola, 2020), yet they can leverage the level of autonomy in AR to sustain ongoing interaction with customers. It is vital to design an autonomous AR that focuses on users’ needs, desires and well-being (de Bellis and Johar, 2020) that drive novel experiences (Novak and Hoffman, 2019). This study recommends AR developers design autonomous features in AR that enable customers to interact with the virtual contents generated by AR and extend their own capabilities to perform a task and feel expanded. While designing a fully autonomous system may hinder users to feel in control of their choice (Schmitt, 2019), service companies can develop an AR system that sustains an interaction, involves the user in value co-creation and guides the user (Alimamy and Gnoth, 2022). AR can sustain an interaction with the users by continuously scanning the objects in the reality and providing sensory feedback such as product size recommendations (e.g. eyeglasses) that facilitate customers’ information processing (Poushneh, 2021b; Heller et al., 2019). To achieve this, developers may focus on technology qualities such as “image recognition,” a subset of AI. With image recognition, AR can effectively provide instruction as if the customer is in a real setting. The proper incorporation of image recognition in the design of AR while enabling users to interact with 3D virtual images sustains their interaction with AR and makes them feel in control of their interaction with AR. Service companies need to ensure users feel in control of their interaction and expand their capacities to engage in the service experience with AR to accomplish their desired tasks. AR’s capacities enable users to expand their abilities to fix their basic service problems without referring to or speaking to a service provider agent in a service context. Therefore, instead of taking their car back to the dealership, customers can use AR mobile applications or glasses provided by car manufacturers to learn and fix basic vehicle problems.

This research advances the marketing literature on how users feel in control of virtual content when they interact with a semiautonomous AR that subsequently influences enriched user experience, perceived augmentation experience, attitudes and behavior.

The purpose of this study is to examine the factors that may have an influence on the continuous intention to use mobile money as one of the Financial Technology (FinTech) products beyond the COVID-19 pandemic lockdown period.

This study has empirically tested the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) to explain the mobile money adoption in Jordan. Data collected were analyzed through partial least squares–structural equation modeling (PLS-SEM).

The results mainly showed that the quality of administrative services and trust significantly impact confirmation and perceived usefulness. In addition, perceived security and knowledge of mobile money as one of the FinTech services significantly influence users’ confirmation and perceived usefulness. Also, usefulness and satisfaction influence continuous intention. Significant relationships were noted among confirmation, perceived usefulness, satisfaction and continuous intention to use mobile money.

This paper integrates two key theories: the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) in the post-adoption behavior of mobile money. Therefore, this study attempts to fill a literature gap by examining the antecedent factors that influence the continuous intention to use mobile money services in the post-consumption stage.

This study aims to investigate the factors that influence the continuance intention to use personal cloud storage services among Generation Y.

A quantitative online survey was carried out to collect data from 271 respondents. Structural equation modelling with SmartPLS 4.0 software was used to run the analysis and examine the hypothesized relationships in the research model.

The study revealed that both satisfaction and habit exert a significant influence on continuance intention, whereas self-efficacy does not demonstrate a significant effect. In addition, satisfaction was found to be influenced by confirmation, perceived usefulness and perceived security. Furthermore, confirmation and cloud storage service quality were found to impact perceived usefulness, while confirmation also had an effect on perceived security. However, the hypothesized moderating role of perceived privacy risk in the relationship between perceived usefulness, perceived security and satisfaction was not supported.

This study advances the field by adapting an expanded expectation-confirmation model that delineates the nuanced impacts of habit, user satisfaction and self-efficacy on Generation Y’s continuance intention to use personal cloud storage services. It challenges the conventional wisdom regarding self-efficacy’s influence on technology adoption, offering a more intricate portrayal of its role. This research contributes a distinctive theoretical perspective, emphasizing the complex interplay of factors that inform sustained user engagement with cloud technologies.

This study aims to present the post-occupancy evaluation (POE) of a smart building on a university campus.

Literature review was conducted to report on the smart building concept and POE. A total of 49 performance indicators, classified according to the technical, functional and behavioral elements, were investigated. A case study of a smart building in a university campus was selected to assess the users’ satisfaction through POE. A total of 90 users from the academic and administrative staff of the building were contacted through a Web-based questionnaire survey, while 35 usable responses were obtained.

The findings revealed that the occupants were satisfied with several aspects of the building performance indicators; however, issues related to ventilation, control of thermostats, amount of natural lighting through low-E windows and privacy were identified. Ultimately, the study reveals that, although the adoption of smart technologies has the potential to provide an excellent and comfortable work environment, the efficient selection and/or maintenance of the adopted technologies is crucial.

The concept of smart buildings is perceived to be the future of the industry. Despite this growing need for “smartization,” the performance of a building ultimately depends on how it satisfies the needs of those who use them. The value and implication of this study is inherent in its emphasis on the satisfaction of buildings users in futuristic designs of buildings. The study shows that POEs can be used to assess the satisfaction of users of smart buildings to understand what works, as well as what needs redesign or improvement.

Although E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.

Through a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.

In the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”

Implementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.