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There is a growing mismatch between the skill demands of the industry and the offerings of academia. One way of reducing this mismatch is by improving collaborations between practitioners and instructors using web-networking platforms. However, it is important to understand practitioners’ considerations while collaborating with instructors. Therefore, this study identified these considerations in order to infer inputs for the design of the graphical user interface (GUI) of a web-based platform for connecting instructors and practitioners.

A mixed method was adopted through a survey and focus group. A survey was used to capture practitioners’ considerations while collaborating with instructors for student development, and a focus group helped uncover an in-depth understanding of the study phenomena. The data were analyzed using descriptive and inferential statistics and thematic analysis.

The results show the willingness of practitioners to collaborate with instructors for student development, the ways by which practitioners are willing to meet instructors' course-support needs and their considerations in deciding to do so. Slight differences were observed between the results of the survey and the focus group regarding the ranking of the practitioners’ considerations. The study highlighted demographic differences in practitioners’ considerations when deciding on meeting instructors' course-support needs. The results provide a basis to deduce the GUI inputs of web-networking platforms for connecting instructors and practitioners.

This study revealed practitioners’ design needs and GUI inputs to facilitate the design of web-networking platforms for connecting instructors and practitioners. This study also contributes to user interface design principles, theories on individual differences and practitioners’ involvement in student professional development.

In this chapter, the authors envision a new framework for technology-enabled local engagement. This framework would exploit web-based collaboration technology in order to create local engagement panels that represent a cross section of ordinary residents. For concreteness, the authors illustrate the framework in a proposed study called “Clearing the Air” would enable residents in the areas of Southern California that are most impacted by the logistics industry to have substantive and constructive opportunities to engage local officials on the complex economic and environmental matters related to logistics and emissions in the Southern California region. The authors propose methods to evaluate best practices in the use of collaboration technology, in particular, to learn whether the technology enables and empowers the engagement panels to participate in air quality governance at a high level and helps local officials better understand the considered opinions of residents on these important matters. If successful, this framework would integrate the policy views of individual residents alongside those of organized stakeholders, experts, and agency officials in the policy process.

This study aims to explore how an accessibility database (AD) has been developed and implemented as a tool for facility managers to evaluate and increase the accessibility of public facilities.

Eight participants were strategically sampled for semi-structured interviews, and documents on the AD were gathered. The Consolidated Framework for Implementation Research (CFIR) was used for a directed content analysis of the data. The CFIR domains used for the analysis were: intervention characteristics, outer setting, inner setting, characteristics of individuals and process.

The development and implementation of the AD demonstrated the complexity in assessing and planning for increased accessibility. The communication and iterative processes within the inner as well as with the outer setting was an important part of the development and implementation, as well as anchoring each step locally, regionally and nationally, within public authorities and disability organizations.

The assessments of environmental barriers and the results reported in the AD can serve as a guide for identification of accessibility issues. However, singular identified barriers were reported as a fragmentation of the building regulations, and thereby when retrofitting is carried out, experts who have the competence to suggest solutions based on the entirety need to be involved to reach the goals of increased accessibility and countering of exclusion and discrimination.

By structuring the implementation process by means of the CFIR, facilitators and barriers of using an AD as a basis for retrofitting were revealed. The practical challenges outlined in assessing and increasing accessibility can guide facility managers when considering actions to increase accessibility.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

The main purpose of this study is to contribute to the literature concerned with improving the effectiveness of corporate social responsibility (CSR) advertising by considering how such ads are pre-tested.

Two similar video ads were produced: one using an informative appeal and the other using an emotional appeal. The latter appeal is more widely used by practitioners. Each ad was designed to promote the CSR credentials of the same (fictitious) company. A web-based experiment (n = 244) was used to test both using two types of measure: first attitude towards the company (such as its image) and second the feelings evoked by the ad.

As predicted from theory, the ads promoted similar evaluations of the company but the evaluations measured by evoked feelings differed significantly. The information-based ad evoked more positive emotions, less negative emotions and more positive attitudes toward the ad. If the ads had been pretested using only measures of evoked feelings, the more emotive treatment would have been rejected.

The study shows why CSR ads should be pretested and why such tests should include multiple measures. It also illustrates how informative CSR video ads can be better received but how both informative and emotional appeals can be used when communicating a company's CSR.

There is little research relevant to the pretesting of ads designed to communicate a company's CSR. Signaling theory can help explain why comparable (CSR) video ads can be evaluated as similar in their effect on company related evaluations.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

This study aims to convey lessons learned from two sustainability initiatives at Norway’s largest university. This contributes to knowledge-based discussions of how future, sustainable higher education institutions (HEIs) infrastructures should be envisioned and planned if the fundamental uncertainty of the future development of learning, researching and teaching is acknowledged.

This study was submitted on 24 January 2023 and revised on 14 September 2023. HEIs, particularly when they are engaged in research activities, have a considerable environmental footprint. At the same time, HEIs are the main producers and disseminators of knowledge about environmental challenges and their employees have a high awareness of the urgent need to mitigate climate change and biodiversity loss. In this study, the gap between knowledge and environmental performance is addressed as a question of infrastructural change, which is explored in two case studies.

The first case study presents limitations of ambitious, top-down sustainability planning for HEI infrastructures: support from employees and political support are central for this strategy to succeed, but both could not be secured in the case presented leading to an abandonment of all sustainability ambitions. The second case study exposes important limitations of a circular approach: regulatory and legal barriers were found against a rapid and radical circular transformation, but also more fundamental factors such as the rationality of an institutional response to uncertainty by rapid cycles of discarding the old and investing in new equipment and facilities.

Being based on qualitative methods, the case studies do not claim representativity for HEIs worldwide or even in Norway. Many of the factors described are contingent on their specific context. The goal, instead, is to contribute to learning by presenting an in-depth and context-sensitive report on obstacles encountered in two major sustainability initiatives.

Research reporting on sustainability initiatives too often focuses descriptively on the plans or reports the successes while downplaying problems and failures. This study deviates from this widespread practice by analysing reasons for failure informed by a theoretical frame (infrastructural change). Moreover, the juxtaposition of two cases within the same context shows the strengths and weaknesses of different approaches to infrastructural change particularly clearly.

Despite the significant growth in Islamic economies and the increasing number of Muslim youths inclining digital services, empirical-based research addressing the adoption of digital Islamic services is still limited. ZakaTech is a new phrase that has recently emerged as a modern term describing novel technologies adopted by zakat institutions; yet, it has been largely neglected in the literature. Therefore, this study aims to provide an integrated model that scrutinizes the determinants of acceptance and use of technology (UTAUT) of ZakaTech, combined with social cognitive theory (SCT), in the midst of the COVID-19 crisis where social distancing is the norm in conducting economic activities.

Based on cross-national evidence from two Muslim-majority countries, a total of 1,006 valid responses were collected from zakat payer users in Saudi Arabia and Egypt using a Web-based survey. To validate the research model and draw significant insights, SmartPLS structural equation modeling was used.

By analyzing both Saudi and Egyptian samples, the authors found that all UTAUT constructs are statistically significant, except for effort expectancy in Egypt. The effects of self-efficacy and social isolation on ZakaTech adoption are supported across both countries. Trust in technology reduces users’ inherent risks and increases their likelihood of adopting ZakaTech services in Saudi Arabia, while this is not the case in Egypt. However, the study revealed that trust in electronic-zakat systems (EZSs) is a vital predictor for mitigating perceived risk among Egyptian users of ZakaTech, but it is not the case in Saudi Arabia. Moreover, significant gender differences were found between males and females in the adoption of such digital services in both countries, particularly regarding self-efficacy, trust in EZSs, social isolation and social influence.

The results provide meaningful insights for policymakers to find ways to develop strategies to escalate the adoption of technology in zakat administration and also to create awareness among the users of ZakaTech in a gender-balanced manner that will include zakat payers and recipients as well in the hope that the digital gender divide will be bridged. Bridging the digital gender divide in this regard is imperative for sustainable inclusive development of zakat. Further, strategies need to be developed to provide incentives to zakat authorities and zakat organizations that adopt technology in managing zakat. This is important to escalate the process of fusing technology with zakat, which is an important social finance tool to eradicate poverty in the world.

This research serves as a building block for literature by empirically testing an integrated model of UTAUT-SCT within a modern and unique related context like ZakaTech. Also, it adds value by testing gender disparities in ZakaTech adoption among Muslims.

Facilities management (FM) activities affect citizens’ health and safety, long-term urban environmental sustainability and the relationships between the built and natural environments of cities. This study aims to comprehend the status of smart FM tools application, how they are used to improve the delivery of FM services and the barriers to their implementation.

To investigate the current smart solutions that leverage the quality of FM service, a case study based on the FM practice in Hong Kong was carried out. The case study was conducted in two phases of data acquisition based on a qualitative research methodology. After conducting in-depth interviews to determine the application of smart FM tools in different types of properties and to identify the initiatives and barriers to smart FM tool application, three workshops were conducted to validate the findings and further investigate the influence of FM professionals on smart FM tools application in Hong Kong.

The findings of the case study revealed, firstly, that four types of smart FM tools – user-centric, safety and hygiene, maintenance and sustainability-oriented – are used to assist the delivery of FM services. Secondly, smart FM tools are shown to be useful in assisting FM activities. Thirdly, the existing barriers to smart FM tool application manifest differently in shopping malls, office buildings and residential buildings. Fourthly, smart FM tools are used to address the four attributes of user needs: comfort, health and wellbeing, convenience and information to occupants.

The value of this study lies in its focus on the industry level (FM industry) and the application process of smart FM tools in different types of property, revealing the benefits, initiatives and barriers to their future application. This study provides a comprehensive picture of the current status and elaborates the barriers to smart FM tool application, which will help FM practitioners to make strategic decisions on selecting and developing smart FM tools. Also, this study will facilitate smart FM tool application policy development.