Proposed Research Framework Research Articles

User Acceptance of Interactive Student Activities Management System: A Proposed Theoretical Model

Interactive Student Activities Management System (iSAMS) is a web-based system in Universiti Teknologi MARA Cawangan Melaka that provides information on student bodies and activities. iSAMS developed to react as an integrated management platform during the COVID-19 outbreak since student activities are essential and need to carry out. This research's main objective was to investigate how the iSAMS interface and functions affect users' intentions and attitudes when using the system. Thus, we explored the key predictors of user acceptance of iSAMS and proposed the theoretical model for user acceptance. A conceptual structural model was developed by integrating heuristic evaluation (HE) and technology acceptance model (TAM) to identify which key predictors influence the user acceptance of iSAMS. We adopted HE and TAM in developing the model, and as the outcome, we categorized HE into three categories: system support, user interface design, and navigation. Then, we identified three of TAM’s predictors, which are perceived usefulness, perceived ease of use, and behavioural intention to use. The hypothesis supported each of the predictors as the measurement since we required improvements for the iSAMS usability. The proposed research framework is simple to implement and can be adapted for other developed systems since it is essential in determining the system's usability and user acceptance.

Green Technology Collaboration Network Analysis of China’s Transportation Sector: A Patent-Based Analysis

The development of green transportation technologies in China has grown rapidly due to increasing concerns about climate change and environmental pollution. Collaboration innovations covering kinds of participating entities and various linked relationships have become one of the critical drivers for the transportation sector. Some researchers have analysed the collaborative innovation of scientific literature in this field. However, fewer studies have investigated the current performance of collaborative technology innovation represented by patents in the transportation sector. In this context, a research framework based on the social network analysis approach is proposed for collaboration green transportation technologies. The purpose of the research is to establish an analytical framework for the green transportation innovation network and seek the key collaboration activities and strategies. Subsequently, a collaborative innovation network based on the patent data of green transportation technologies was built and analysed. Especially, the innovation entities in the collaboration network are divided into four groups: business enterprises, individuals, universities, and research institutions, so that more detailed information in the network could be obtained. The results show that the proposed research framework based on patent data and social network analysis method helps examine the critical nodes and links in the network, as well as their types and characteristics of the collaboration network. The increasing number of green transportation technologies shows active cooperation in this field. The study also found that business enterprises node gradually plays a major role in cooperative innovation. The corresponding policy recommendations are also provided.

The Predicted Trainer and Training Environment Influence toward Vocational Training Effectiveness in Bahrain

This research aims to examine the development of vocational training issues faced in the public sector extent in Bahrain. Training implication should not be overlooked in delivering the required capabilities and knowledge in performing a certain task. The proposed research framework is established with a reference to the training evaluation model of Kirkpatrick, however, the study analyzes one of four levels for evaluating training effectiveness, those are reaction, learning, behavior, and the level of interests; result. A quantitative research design is applied. The survey instrument comprised of 29 items that tested two hypotheses. It was distributed to 382 respondents according to their interests using the purposive sampling technique adopted across 4 different public sector organizations in Bahrain. However, 128 were the usable ones from the returned 155 questionnaires. It is discovered that the contextual factor namely trainer has a positive influence on the training programmes implemented in this sector. Also, surprisingly this study unlocks an interesting and challenging area in the training environment for scholars in exploring and improving the quality of training programmes. The study essentially contributes to the paucity in training antecedents and training effectiveness in the public sector literature-related research. Hence, enhancing training effectiveness requires the management and practitioners to enhance the training antecedents to ensure the achievement of training/organization objectives. Understanding the importance of these certain types of factors will help the management to enhance the trainees’ gaining and therefore their performance. Thus, training antecedents should play an important role before trainee’s training. Managers should as well be involved with sponsoring reliable factors and drawing up a comprehensive vision.

Regulation of Data-driven Market Power in the Digital Economy: Business Value Creation and Competitive Advantages from Big Data

Recent high-profile antitrust cases and policy proposals have put a spotlight on the relationship between firms’ access to big data and sustained competitive advantages in digital markets. In Europe, concerns about data-driven market power have led policy makers to propose far-reaching regulations of information technology (IT) in these markets. Despite the global policy relevance, the regulation of big data and its competitive effects has so far received little attention in information systems (IS) research. This article addresses this research gap by developing an overarching framework for future IS research on the role of IT for the regulation of data-driven market power. The proposed research framework builds upon a three-part analysis: First, we review the academic literature and show that there is extensive, although nuanced, empirical evidence for business value creation from big (user) data. Second, we draw on the resource-based view of the firm and recent policy reports to derive six facilitating factors that enable firms to establish market power based on sustained data-driven competitive advantages. Third, we characterize three regulatory approaches to govern market power and competition in data-driven digital markets: (i) empowering consumers, (ii) data openness and (iii) limiting data scale. For each of these approaches, we highlight the key role of IT artifacts in mediating the effect of regulatory rules on actual practice.

Restaurant preventive behaviors and the role of media during a pandemic.

This study explores how the preventive behaviors of restaurant customers towards COVID-19 are shaped by exposure and attention to media coverage, thereby connecting the issues of community anxieties and business resilience during crises. Ways in which media exposure and attention predict COVID-19 preventive behaviors were examined, as these relate to emotional fear responses and cognitive risk perceptions. An online survey was administered in Taiwan during the COVID-19 pandemic to test the proposed research framework. A total of 366 responses were collected using convenience sampling, and structural equation modeling was deployed to examine the hypothesized relationships. Results indicate that consumer fears and risk perceptions were positively influenced by media coverage of COVID-19. Moreover, fear positively affected individual risk perceptions, and risk perception positively influenced restaurant preventive behaviors. Risk perception was also identified as a mediator between a) media exposure and restaurant preventive behaviors and b) media attention and restaurant preventive behaviors.

Mainstreaming natural flood management: A proposed research framework derived from a critical evaluation of current knowledge

Natural flood management (NFM), or working with natural processes, is a growing flood risk management method in the UK, Europe and worldwide. However, unlike the current dominant technical flood management, it lacks an established evidence base of flood risk parameters. This lack of evidence base can limit the uptake of NFM as a flood management method. This paper critically evaluates examples of NFM and wider relevant literature in order to identify NFM knowledge gaps and suggest how to overcome these. The UK is used as a microcosm of different environments for diverse examples. The sections include: land cover, land management, landscape interactions and trade-offs, evaluating the wider benefits of NFM and, finally, scaling from plot to catchment. This concludes in a suggested framework for a new approach to NFM research, which encompasses spatial scales, interactions and trade-offs of NFM and consistency of reporting results. Widening the NFM empirical evidence base should be seen as an opportunity for a new approach to flood research through exploring new habitats and new flood resilience methods.

Application of Exact and Multi-Heuristic Approaches to a Sustainable Closed Loop Supply Chain Network Design

Closed-loop supply chains (CLSC) are gaining popularity due to their efficiency in addressing economic, environmental, and social concerns. An important point to ponder in the distribution of CLSC is that imperfect refrigeration and bad road conditions may result in product non-conformance during the transit and thus such products are to be returned to the supply node. This may hinder the level of customer satisfaction. This paper presents a sustainable closed-loop supply chain framework coupled with cross-docking subject to product non-conformance. A cost model is proposed to investigate the economic and environmental aspects of such systems. The transportation cost is analyzed in terms of total carbon emissions. A set of metaheuristics are administered to solve the model and a novel lower bound is proposed to relax the complexity of the proposed model. The results of different size problems are compared with the branch and bound approach and the proposed lower bound. The results indicate that the proposed research framework, mathematical model, and heuristic schemes can aid the decision-makers in a closed-loop supply chain context.

Ensemble projection of city-level temperature extremes with stepwise cluster analysis

Climate change can cause property damage and deaths in cities. City-scale climate projections are essential for making informed decisions towards climate change mitigation and adaptation at city levels. This study aims at developing ensemble projections of temperature extremes at the city-level and quantifying the contributions of various factors to the resulting uncertainty of the ensemble projections. The city of Toronto will be used here as an example to demonstrate the effectiveness of the proposed research framework. In particular, the stepwise cluster analysis (SCA) model will be used to perform climate downscaling to three GCM datasets (GFDL, IPSL, and MPI) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in order to generate city-level climate projections for the city of Toronto. The SCA model is demonstrated to be capable of capturing the inter- and intra-annual variations of the daily maximum, mean, and minimum temperatures in the studied city. The results suggest that mean temperatures in Toronto are projected to increase at the rate of 0.15 and 0.5 °C/decade under RCP4.5 and RCP8.5, respectively, while no significant warming trend is detected for RCP2.6. In terms of temperature extremes, extreme warm events are projected to increase while extreme cold events decrease under all emission scenarios. The decrease in the heating demand is two to four times larger than the increase in the cooling demand, indicating a decrease in the city’s total energy use. The projected warming might be beneficial for the urban growers because of the significant increases in the growing season length and growing degree days; however, the residents of the city of Toronto are likely to experience simultaneous increases in the intensity, duration, and frequency of heatwave events in future summers. Because of the warming, coldwave events in winters are likely to become less frequent and be shorter in duration, but their intensity is expected to increase significantly. Through decomposition of the resulting uncertainty of the ensemble projections, emission scenario is found to be the dominant factor for the uncertainty associated with urban climate projection.

Content analytics based on random forest classification technique: An empirical evaluation using online news dataset

In this paper, a study is established for exploiting a document classification technique for categorizing a set of random online documents. The technique is aimed to assign one or more classes or categories to a document, making it easier to manage and sort. This paper describes an experiment on the proposed method for classifying documents effectively using the decision tree technique. The proposed research framework is a Document Analysis based on the Random Forest Algorithm (DARFA). The proposed framework consists of 5 components, which are (i) Document dataset, (ii) Data Preprocessing, (iii) Document Term Matrix, (iv) Random Forest classification, and (v) Visualization. The proposed classification method can analyze the content of document datasets and classifies documents according to the text content. The proposed framework use algorithms that include TF-IDF and Random Forest algorithm. The outcome of this study benefits as an enhancement to document management procedures like managing documents in daily business operations, consolidating inventory systems, organizing files in databases, and categorizing document folders.

Understanding tourist mobile hotel booking behaviour: Incorporating perceived enjoyment and perceived price value in the modified Technology Acceptance Model

This study examines how mobile technology adoption influences customers' intention to book hotel rooms via smartphone. This study empirically incorporated perceived enjoyment and perceived value in the modified Technology Acceptance Model (m-TAM) and tested it as a unified model. Partial Least Square-Structural equation modelling (PLSSEM) was applied to estimate the proposed research framework based on the survey data collected from 386 travellers who booked hotels via their smartphones. The structural model confirms perceived usefulness, perceived ease of use, perceived enjoyment, and perceived price value significantly influence consumers' behavioural intentions toward mobile hotel booking. This study confirms that TAM can be extended and employed to predict and explain the acceptance of the new technologies in service industries. This study also provides valuable theoretical contributions in developing and testing related theories and practical implications to hotel operators, online travel agencies (OTAs), and hospitality technology suppliers.

Auditor judgment and decision-making in big data environment: a proposed research framework

PurposeThe role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.Design/methodology/approachThe methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.FindingsThe proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.Research limitations/implicationsThere is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.Originality/valueWith the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.

ارائه چارچوبی برای مدیریت مقابله با قاچاق سوخت ایران

Difference between subsidized fuel prices inside and outside the country, create good opportunity for trafficking in fuel by smugglers. A major challenge is happen in supply and distribution cycle of fuel by this problem. Then, providing a framework for managing subsidy fuel is essential in economy of the country.In the first step in this research, the root cause of the smuggling of fuel was identified by the fish boned method and reading studies, field studies, interview and Delphi method. Then, the cause and effect relation of the factors was drawn with DEMATEL method. Then, the solutions were presented to eliminate these factors through mental storm meetings and interviews with oil industry experts. At the final step, ranking of solutions for resource allocation are done by the hybrid AHP-DEMATEL method.At each stage of the proposed research framework, the results obtained were certified by oil industry experts. This is a solid proof of the credibility of the proposed framework for the elimination of fuel smuggling in the country.

How home country identity salience affects emerging market companies' overseas IPO performance: an empirical investigation from China

PurposeThe purpose of the current study is to examine how and when home country identity salience of emerging market companies affects their overseas initial public offering (IPO) performanceDesign/methodology/approachBy using secondary data from multiple sources, this study empirically tests the proposed research framework in the context of Chinese companies' overseas IPO activities in the US stock markets.FindingsThe results demonstrate that home country identity salience positively affects overseas IPO performance, and thus can be recognized as the asset of foreignness. Cultural specification positively moderates the effect of home country identity salience on overseas IPO performance. Market internationalization also plays an important moderating role in the relationship between home country identity salience and overseas IPO performance.Originality/valueThe current study identifies a new factor, that is, home country identity salience, that can mitigate the liability of foreignness for emerging market companies in their overseas IPO activities. The study also documents the positive cultural impacts on overseas investors in a financial and international context.

Brain tumor detection and multi‐classification using advanced deep learning techniques

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

No Limits of Acceptable Change: A Proposed Research Framework for Informing Visitor Use Management in the Context of Cultural Resources

Cultural resources are commonly defined as resources that provide material evidence of past human activities. These resources are unique, as they are both finite and non-renewable. This provides a challenge for traditional visitor use management since these resources have no limits of acceptable change. However, with nearly every national park in the US containing cultural resources, coupled with ever-growing visitation, it is essential that managers of parks and protected areas have the ability to make science-informed decisions about cultural resources in the context of visitor use management. We propose a framework that can help provide context and exploration for these challenges. Drawing on previous literature, this framework includes risk-based approaches to decision making about visitor use; visitor cognitions related to cultural resources; emotions, mood, and affect related to cultural resource experiences; creating and evaluating interpretive programs; deviant visitor behaviors related to cultural resources; and co-management.

Social distancing close together: the rhizomatic role of WhatsApp in communities - a proposed research framework

As covid-19 hits its peak, people who are not in close physical proximity but who do communicate regularly on social platforms such as WhatsApp groups often claim to feel a sense of closeness to others. This paper will consider the features and functioning for WhatsApp groups in terms of Preece's (2000) characteristics of a virtual community: people, purpose, politics and computer systems, and filter them through Deleuze and Guattari's (1987) six principles of the rhizome: connectedness, heterogeneity, multiplicity, asignifying rupture, cartography and decalcomania to see the relationship between the principles of the rhizome and the sense of closeness in a virtual community. Finally, a six by four matrix is proposed for the analysis of various aspects of virtual communities.

Assessing brand love, brand sacredness and brand fidelity towards halal brands

PurposeThe purpose of this study is to examine the role of contemporary consumer-brand relationships in predicting brand sacredness, brand fidelity and propensity to spread word of mouth (WOM) in the context of “halal” cosmetic brands.Design/methodology/approachThe proposed research framework has been empirically tested by collecting responses from 403 Muslim respondents of diverse demographics. Structural equation modelling has been used for exploring the underlying relationships between emotions associated with halal brands.FindingsAll the proposed hypotheses were positively significant, thus confirming that brand love is significantly affected by brand trust, brand image, self-congruence and brand experience. Further, brand love acts as a significant determinant in shaping brand sacredness, brand fidelity and WOM.Research limitations/implicationsThe study has made a significant contribution by exploring the intensity of brand love and its effect on relationship-maintenance triad in halal cosmetic brands in India.Practical implicationsMarketers must understand the emotional side of brands to create synergy in their marketing efforts. Moreover, in the case of religious or faith-based brands, attainment of the highest order of brand love can play a revolutionary role for long-term growth.Originality/valueIn the context of halal cosmetic brands in India, this study has offered a new perspective by extending the literature on consumer-brand relationships. The use of brand fidelity and brand sacredness has enhanced the effectiveness of emotions with which consumers have been looking at brands for ages.

Knowledge Transfer Enablers and Innovation Performance of International Strategic Alliances: An Integrated Framework

Knowledge Transfer Enablers and Innovation Performance of International Strategic Alliances: An Integrated Framework

Changing role of lifestyle in tourism entrepreneurship: Case study of Naked Retreats Enterprise

Despite increased academic attention to tourism lifestyle entrepreneurship (TLE), different discourses and continuous controversy have impeded an understanding of the phenomenon. This paper seeks to provide a new and in-depth understanding through a case study. We select the case of Naked Retreats Enterprise (NRE), a well-known tourism lifestyle corporation in China that has distinctive and deviant TLE. Based on the proposed research framework and multiple-source longitudinal data, we described and interpreted the change in the lifestyle role during the tourism entrepreneurial process. The findings suggest that the role of lifestyle in NRE changed from entrepreneurial motivation to a customer product and then to enterprise capital. The change dynamic is complex and related to contextual factors. This research verifies the effectiveness of a case study and features a dynamic lens and contextual sensibility as applied to TLE research. Moreover, and more broadly, the possibility of bridging the isolated lifestyle discourse in tourism is reflected.

Can Recommender Systems Reduce Healthcare Costs? The Role of Time Pressure and Cost Transparency in Prescription Choice

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.