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1 – 10 of over 12000Poor indoor air quality (IAQ) contributing to occupants’ health symptoms is a universal, typically ventilation-related, problem in schools. In cold climates, low-cost strategies…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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Janakiraman Moorthy, Sheena Choi and Prasad Bingi
We investigated the effectiveness of using feature films in teaching organizational behavior courses at the undergraduate level at a mid-Western university in the USA.
Abstract
Purpose
We investigated the effectiveness of using feature films in teaching organizational behavior courses at the undergraduate level at a mid-Western university in the USA.
Design/methodology/approach
Our model included the impact of film analysis on self-perceived learning outcomes and cognitive and affective changes among students. Structural equation modeling using partial least squares and contemporary mediation analysis techniques were employed.
Findings
Featured film analysis positively impacted perceived learning outcomes and the cognitive and affective components of learning among students. We also found an indirect effect on cognitive and affective change, indicating that learners’ improved perceived learning outcomes deepened their learning and resulted in greater appreciation of organizational behavior theories.
Practical implications
Films are effective pedagogical tools for teaching complex business theories and principles. We recommend that faculty members pay careful attention to selecting films for study and should design film analysis projects aligned with meaningful course learning outcomes. Appropriate films and carefully designed learning outcomes trigger cognitive changes and have a lasting influence on students beyond the semester.
Originality/value
Our study is one of the few empirical studies demonstrating the effectiveness of feature films as a pedagogical tool for organizational behavior courses.
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The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether social media affordances and media richness as…
Abstract
Purpose
The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether social media affordances and media richness as environmental stimuli to learners’ involvement elicited by massive open online courses (MOOCs) can affect their learning persistence in MOOCs and, in turn, their learning outcomes in MOOCs. This study further examines whether demographic variables can moderate the relationship between learners’ learning persistence in MOOCs and their learning outcomes.
Design/methodology/approach
Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 396 usable questionnaires were analyzed using structural equation modeling.
Findings
This study proved that learners’ perceived social media affordances and media richness in MOOCs positively influenced their cognitive involvement and affective involvement elicited by MOOCs, which concurrently expounded their learning persistence in MOOCs and, in turn, uplifted their learning outcomes in MOOCs. The results support all proposed hypotheses and the research model, respectively, explains 70.5% and 61.8% of the variance in learners’ learning persistence in MOOCs and learning outcomes. Besides, this study showed that learners’ usage experience moderated the relationship between learners’ learning persistence in MOOCs and their learning outcomes.
Originality/value
This study uses the S-O-R model as a theoretical groundwork to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is affected by social media affordances and media richness. Noteworthily, while the S-O-R model has been extensively used in previous literature, little research uses the S-O-R model to explain the media antecedents of learners’ learning persistence and learning outcomes in MOOCs. Hence, this study enriches the research for understanding how learners value their learning gains via using media features to support them in MOOCs.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Zhongyun Zhou, Zidie Chen and Xiao-Ling Jin
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…
Abstract
Purpose
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.
Design/methodology/approach
The authors conduct a literature review on the definition, technical features and user behaviors of/in the metaverse.
Findings
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.
Originality/value
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.
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Doan Thao Tram Pham, Sascha Steinmann and Birger Boutrup Jensen
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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).
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Kerim Koc, Ömer Ekmekcioğlu and Asli Pelin Gurgun
Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Social implications
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.
Originality/value
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.
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Indranil Ghosh, Rabin K. Jana and Mohammad Zoynul Abedin
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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Yu-Jen Chou, Ya-Hui Hsu and Yu-Han Chang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Shakiba Kazemian and Susan Barbara Grant
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.
Abstract
Purpose
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.
Design/methodology/approach
The methodology uses genre analysis and grounded theory to analyse empirical data from posts obtained through Microsoft Yammer and a focus group.
Findings
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.
Practical implications
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.
Originality/value
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.
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