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This paper aims to examine how gamification components affect training outcomes through intrinsic or extrinsic motivation (IM and EM), drawing on the self-determination motivation theory.

In this study, survey method has been used to analyse the hypotheses and objective of the research. A total of 260 surveys were received through the web-based stage and 260 surveys were legitimate. The data in this study was investigated using SPSS version 20.0 and Smart-PLS version 3.0 software.

The findings represent how IM intervenes in gamification parts of training outcomes. Apart from the indirect effect, this study also shows the immediate effect of experience point and progress bar affecting IM and EM. This study shows that the immediate effect of IM has a positive impact on training outcomes, however there is an adverse consequence in the event of EM on training outcomes as well as there is no intervening or mediating impact.

In this study, the authors offer novel research that might aid businesses in identifying the most important aspects of gamification for the relevant personnel. There is a substantial correlation between gamification and employee engagement that was previously focused on. With particular emphasis on the progress bar and experience point, the authors have demonstrated a connection between IM and EM through the use of gamification elements, paving the way for businesses to place a greater emphasis on intrinsic drive-in gamification systems intended to enhance employee training.

Poor indoor air quality (IAQ) contributing to occupants’ health symptoms is a universal, typically ventilation-related, problem in schools. In cold climates, low-cost strategies to improve IAQ in a naturally ventilated school are rare since conventional methods, such as window opening, are often inappropriate. This paper aims to present an investigation of strategies to relieve health symptoms among school occupants in naturally ventilated school in Finland.

A case study approach is adopted to thoroughly investigate the process of generating the alternatives of ventilation redesign in a naturally ventilated school where there have been complaints of health symptoms. First, the potential sources of the occupants’ symptoms are identified. Then, the strategies aiming to reduce the symptoms are compared and evaluated.

In a naturally ventilated school, health symptoms that are significantly caused by insufficient ventilation can be potentially reduced by implementing a supply and exhaust ventilation system. Alternatively, it is possible to retain the natural ventilation with reduced number of occupants. The selected strategy would depend considerably on the desired number of users, the budget and the possibilities to combine the redesign of ventilation with other refurbishment actions. Furthermore, the risk of poorer indoor air caused by the refurbishment actions must also be addressed and considered.

This study may assist municipal authorities and school directors in decisions concerning improvement of classroom IAQ and elimination of building-related symptoms. This research provides economic aspects of alternative strategies and points out the risks related to major refurbishment actions.

Since this study presents a set of features related to indoor air that contribute to occupants’ health as well as matters to be considered when aiming to decrease occupants’ symptoms, it may be of assistance to municipal authorities and practitioners in providing a healthier indoor environment for pupils and teachers.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.

Despite the significance of online communication and interactions, previous research has not systematically compared all features on a single platform from the users' perspective. This study aims to fill this gap by extensively reviewing the current literature on social media affordances and proposes and tests a feature-centric and affordance-based conceptualization of social media platforms (SMPs) between users, features, the audience and content.

This research surveys users on Facebook, one of the largest SMPs, and asks them to assess 20 features of Facebook on six relational affordances between users, features, audience and content. The data in this study were collected on Amazon Mechanical Turk (MTurk) with participants from the US Correspondence analysis was employed to examine the relationship between affordances and the ties among affordances, features and outcomes.

Results of the study indicate that users perceive features differently, and employing features as the unit of analysis captures users' interactions effectively. The findings support the presence of user-oriented affordances, such as presentation flexibility, association and content association. These three affordances can be summarized in two higher-level ones: self-expression and connection (SEC) and persona-linked content (PLC). Our findings of the two dimensions, SEC and PLC, highlight the importance of targets and their connections in understanding social media interactions' dynamic nature.

By proposing to shift the focus from platforms to features, this study suggests that companies should focus on understanding the features they use for their users to interact with their brand, rather than merely ensuring that their company is omnipresent on all platforms. This study underlines the need to focus on features that will help managers influence interpersonal and user-brand communications and interactions on social media.

This research is the first to put features at the center of its investigation and quantitatively examine the relationship between social media features and affordances in a social media context. In all, this research provides a new unit of analysis that is more suitable for researchers to build a robust conceptual foundation for affordances. We believe that conceptualizing audience and content as outcomes, distinguishing it from features and creating connections between them as affordances is the unique aspect of our conceptualization.

As a sociotechnical system, the metaverse has sparked heated discussion. However, concerns abound that the concept is “old wine in a new bottle” used for capital hype. The mixed definitions of the metaverse and unclear relationships between its technical features and user behaviors have greatly impeded its design and application. Therefore, the authors aim to sort out the metaverse definition and properties, analyze its technical features in various contexts and unveil the mechanisms leading to user behaviors.

The authors conduct a literature review on the definition, technical features and user behaviors of/in the metaverse.

First, the authors identify two main categories of the metaverse definition and find a mixed conceptualization. Second, the authors present technologies and technical features in the diverse contexts of the metaverse. Third, the authors summarize the effect of technical features on user behaviors from a sociotechnical perspective.

The authors analyze the definition, technical features, user behaviors of the metaverse and their theoretical foundations. Based on these findings, the authors propose a theoretical framework unveiling how social and technical elements affect user behaviors in the metaverse. In conclusion, the study offers a research agenda for future studies.

In this paper the authors aim to review the state-of-the-art literature on online review systems and their impacts on consumer behavior and retailers' performance with the aim of identifying research gaps related to different design features of review systems and developing future research agenda.

The authors conducted a systematic review based on PRISMA 2020 protocol, focusing on studies published in the domains of retailing and marketing. This procedure resulted in 48 selected papers investigating the design features of retailer online review systems.

The authors identify eight design features that are controllable by retailers in an online review system. The design features have been researched independently in previous literature, with some features receiving more attention. Most selected studies focus on the design features adapted metrics and review presentations, while other features are generally neglected (e.g. rating dimensions). Previous literature argues that design features affect consumer behaviors and retailers' performance. However, the interactions among the features are still neglected in the literature, creating a relevant gap for future research.

This paper distinguishes between different types of retailer online review systems based on how they are implemented. The authors summarize the state-of-the-art of relevant literature on design features of online review systems and their effects on consumer- and retailer-related outcome variables. This systematic literature review distinguishes between online reviews provided on websites controlled by retailers (internal systems) and third-party websites (external systems).

Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.

Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.

The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.

The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.

Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.

The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

This research paper aims to illustrate that the new product communication effects of mental simulation (process-vs. outcome-focused) might depend on product attributes (typicality and benefits). Communication effects include ad attitudes and product attitudes in this study.

One 2 (mental simulation: process-focused vs. outcome-focused) x 2 (attribute typicality: high vs. low) x 2 (attribute benefits: hedonic vs. utilitarian) between-subjects experiment design was conducted. SPSS was used to do data analysis.

This article reveals that high (low) typicality of new attributes causes a process-focused (outcome-focused) simulation to lead to better consumer attitudes (i.e. ad attitude and product attitude). In addition, for a new hedonic attribute, a low typical attribute induces better consumer attitudes. Furthermore, there are interaction among mental simulation, product attribute typicality and benefits. These findings have important implications for academic developments and marketing management.

Compared with previous studies, this study is unique in several ways. First, enterprises often develop new products by introducing new product attributes (i.e. new features). Product attribute typicality is an interesting issue for new product design and communication. This research illustrates that the marketing communication effects of attribute typicality depends on attribute benefits and mental simulation. Second, the current research finds the new product attribute benefit (i.e. hedonic/utilitarian) play an important role and moderates the effects of mental simulation on consumer attitudes.

The paper aims to explore “content” factors influencing consumptive and contributive use of enterprise social networking within UK higher education during the COVID-19 pandemic.

The methodology uses genre analysis and grounded theory to analyse empirical data from posts obtained through Microsoft Yammer and a focus group.

The findings reveal the motivators-outcomes-strategies and the barriers-outcomes-strategies of users. Motivators (M) include feature value, Information value, organizational requirement and adequate organizational and technical support. Barriers (B) include six factors, including resisting engagement on the online platform, emotional anxiety, loss of knowledge, the lack of organizational pressure, lack of content quality and lack of time. An Outcomes (O) framework reveals benefits and dis-benefits and strategies (S) relating to improving user engagement.

The research method and resultant model may serve as guidelines to higher educational establishments interested in motivating their staff and scholars around the use of enterprise social network (ESN) systems, especially during face-to-face restrictions.

This research study was conducted during the COVID-19 pandemic which provides a unique setting to examine consumptive and contributive user behaviour of ESN’s. Furthermore, the study develops a greater understanding of “content” factors leading to the benefits or dis-benefits of ESN use, drawing on user motivators, barriers and strategies during the COVID-19 pandemic in UK education.