This work investigated the suitability of a new tool for decision support in training programs of high performance athletes. The aim of this study was to find a reliable and robust measure of the fitness of an athlete for use as a tool for adjusting training schedules. We examined the use of heart rate recovery percentage (HRr%) for this purpose, using a two-phased approach. Phase 1 consisted of testing the suitability of HRr% as a measure of aerobic fitness, using a modified running test specifically designed for high-performance team running sports such as football. Phase 2 was conducted over a 12-week training program with two different training loads. HRr% measured aerobic fitness and a running time-trial measured performance. Consecutive measures of HRr% during phase 1 indicated a Pearson’s r of 0.92, suggesting a robust measure of aerobic fitness. During phase 2, HRr% reflected the training load and significantly increased when the training load was reduced between weeks 4 to 5. This work shows that HRr% is a robust indicator of aerobic fitness and provides an on-the-spot index that is useful for training load adjustment of elite-performance athletes.

Knowledge management and knowledge assets have gained much prominence in recent years and are said to improve organizational performance. Knowledge capture and acquisition mechanisms enhance organizational memory and performance. However, knowledge capture and acquisition mechanisms in higher education institutions are not well known. The aim of this study was to investigate the knowledge capture and acquisition mechanisms at Kisii University. This was a case study in which data were collected through interviews and questionnaires. Purposive sampling was used to determine interview participants while questionnaire respondents were selected through stratified random sampling. Qualitative and quantitative data were analysed using SPSS® student version 14; it revealed that there were various knowledge capture and acquisition mechanisms at Kisii University. It was also established that the University encountered various challenges in knowledge capture and acquisition and lacked some essential knowledge capture and acquisition mechanisms. In this regard, this study proposed knowledge capture and acquisition guidelines that may be adopted by the University to enhance its organizational memory and performance.

Aim/Purpose: This journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. Background: This journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. Methodology: The qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. Contribution: The preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. Findings: Preliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter’s ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations Recommendations for Practitioners: Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive’s objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for Researchers: The importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary considerations in technology adoption. However, factors that have been identified that impact on use do not express the importance of cognitive processes in technology adoption. Future Research: Future research requires further focus on intangible and psychological factors that could affect technology adoption and use, as well as understanding data visualization effectiveness in corporate environments, not only predominantly within the Health sector. Lessons from Health sector studies in data visualization should be used as a platform.

Aim/Purpose: This explanatory study aimed to determine the mediating role of ERP in the relation between the effect of a socio-technical approach and decision-making environment, and firms’ sustainable performance. Background: Although earlier studies have discussed the critical success factors of the failure or success of an ERP system and the extent to which it achieves its desired objectives, the current study focused on the significant impact of socio-technical elements and decision-making environment on the success of the ERP system (i.e., sustainable performance). In addition, the lack of research on ERP as a mediator in the above relationship motivated this study to bridge the literature gap. Methodology: The data was collected using questionnaires distributed to 233 randomly selected employees of three multinational companies (BP, LUKOIL, and Eni) operating in Iraq. The structural equation modeling was employed to test the hypothesized relationships. Contribution: The study contributes to the literature by examining the mediating role of the ERP system in the relationship between socio-technical elements and the decision-making environment, as well as, the moderating role of organizational culture in the relationship between socio-technical elements and ERP systems. Findings: The results showed that ERP is a significant mediator between the linkage of socio-technical elements and the decision-making environment while organizational culture has an insignificant moderating role in the relationship between socio-technical elements and ERP systems. Recommendations for Practitioners: In a developing country like Iraq, there is a need to implement ERP to achieve better sustainable performance through change management and organizational development that ultimately work towards enhancing individual capabilities, knowledge, and training. Recommendation for Researchers: The researchers are recommended to conduct an in-depth study of the phenomenon based on theoretical and empirical grounds, particularly in light of the relationship of socio-technical elements and decision-making environments. Impact on Society: This study provides a reference for organizations with similar cultural backgrounds in using ERP systems to minimize pollution in Iraqi context. Future Research: A more in-depth study can be performed using a bigger sample, which not only includes the oil industry but also the other industries.

Aim/Purpose: In this research, we examined the influence of the information system (IS) quality dimensions proposed by Wixom and Todd on reading satisfaction of online newspaper readers in Bangladesh, especially the readers’ intention to revisit and recommendations through electronic word-of-mouth (eWOM). Background: We identified the top 50 most visited websites, of which 13 were online newspapers, although their ranking among Bangladesh online newspapers varies from month to month. The literature illustrates that, despite the wide availability of online news portals and the fluctuations in frequency of visits, little is known about the factors that affect the satisfaction, word-of-mouth, and frequency of visits of readers. An understanding of reader satisfaction will help to gain richer insights into the phenomenon of readers’ intention to revisit and recommendation by eWOM. Stakeholders of online newspapers can then focus on those factors to increase visits to their websites, which will help them attract online advertisements from different organisations. Methodology: Data were collected using a structured questionnaire, from 217 people who responded to the survey. We used SmartPLS 3 to analyze the data collected, as it is based on second-generation analysis, which in turn is based on structural equation modeling (SEM). Contribution: This research explores the impacts of technological dimensions on readers’ satisfaction, as most of the previous research has focused on cultural or social dimensions. Findings: The results supported all of the hypothesized relationships between technological dimensions and reader satisfaction with online newspapers, except for one. The first, information, was predicted with accuracy and completeness, while the second object-based belief, system quality, was predicted by its accessibility, flexibility, reliability, and timeliness. Overall, quality factors influencing readers’ satisfaction were shown to lead to word-of-mouth revisit intentions. Our proposed model was empirically tested and has contributed to a nascent body of knowledge about readers’ revisit intentions and eWOM recommendations regarding online newspapers. It was also shown that strong satisfaction leads to higher revisit intention and eWOM. Recommendations for Practitioners: To keep the users satisfied, online newspapers need to focus on improving information quality (IQ) and system quality (SQ). If they do this well, they will be rewarded with higher revisit intention and recommendations by eWOM. Recommendation for Researchers: This study extends Oh’s customer loyalty model by integrating the Wixom-Todd model. This study reinforces an alternative rationale of the construct satisfaction. Future Research: We ignored negative stimulus like technostress, which can have an impact on satisfaction. In future, we will test the relationship between technostress and its impact on online newspaper reading.

Aim/Purpose: This study investigates the issue of knowledge acquisition among faculty members. Background: The paper reports the use of knowledge acquisition tools and reading knowledge sources by faculty members. It also identifies demographic differ-ences among participants in using knowledge acquisition tools and reading knowledge sources. Methodology: The study used an online survey-based questionnaire tool for data collection. The participants consisted of 300 faculty members from 26 academic institu-tions in UAE. Statistical tests are used to verify and validate the hypotheses. Contribution: The paper represents one of the few empirical studies conducted on knowledge acquisition among faculty members in the GCC countries. Find-ings of the study may contribute to the theoretical and practical understanding of knowledge acquisition among faculty members. Findings: Findings of the study revealed that medical faculty members read knowledge acquisition sources more than other faculty members. Likewise, IT faculty members use knowledge acquisition tools more than other faculty members. Results of the study supported stage three of knowledge acquisition proposed in the “Stage Theory of Knowledge Consumption Growth” (Mathew, 1985). The study found that journals are the most sources read by the participants while web-based training (WBT) tools are the most used knowledge acquisition tools among faculty members. Results of the study indicated significant differ-ences among faculty members of different age groups, academic ranks, aca-demic specializations, and institutional affiliation in reading knowledge sources. Likewise, findings of the study revealed significant difference among partici-pants of different academic specializations in using knowledge acquisition tools. Recommendations for Practitioners: Results of the study could be extrapolated to other faculty members in the GCC countries. Recommendation for Researchers: More researches could be done to address different issues of knowledge acquisition among faculty members. Impact on Society: Faculty reading of knowledge sources and use of knowledge acquisition tools may have direct or indirect positive impacts on innovation, creativity, and re-search productivity in any society. Future Research: It will be interesting to apply more than one data collection method in the future research.

Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision. Contribution: The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models. Findings: Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers. Recommendations for Practitioners: Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange. Recommendation for Researchers: The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome. Impact on Society: The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd’s perspective. Future Research: In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.

Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.

Aim/Purpose: The first goal is to develop a decision support system for pricing and production amounts for a firm facing high levels of product returns. The second goal is to improve the management of the product returns process. Background: This study was conducted at a food importer and manufacturer in Israel facing a very high rate of product returns, much of which is eventually discarded. The firm’s products are commonly considered to be a low-cost generic alternative and are therefore popular among low-income families. Methodology: A decision support module was added to the plant’s business information system. The module is based on a supply chain pricing model and uses the sales data to infer future demand’s distribution. Ergonomic models were used to improve the design of the returns warehouse and the handling of the returns. Contribution: The decision support system allows to improve the plant’s pricing and quantity planning. Consequently, it reduced the size of product returns. The new design of the returns process is expected to improve worker’s productivity, reduces losses and results in safer outcomes. This study also demonstrates a successful integration and of a theoretical economical model into an information system. Findings: The results show the promise of incorporating pricing supply chain models into informing systems to achieve a practical business task. We were able to construct actual demand distributions from the data and offer actual pricing recommendations that reduce the number of returns while increasing potential profits. We were able to identify key deficiencies in the returns operations and added a module to the decisions support system that improves the returns management and links it with the sales and pricing modules. Finally, we produced a better warehouse design that supports efficient and ergonomic product returns handling. Recommendations for Practitioners: This work can be replicated for different suppliers, manufacturers and retailers that suffer from product returns. They will benefit from the reduction in returns, as well as the decrease in the losses associated with these returns. Recommendation for Researchers: It is worthwhile to research whether decision support systems can be applied to other aspects of the organizations’ operations. Impact on Society: Product returns is a lose-lose situation for producers, retailers and customers. Moreover, mismanagement of these returns is harmful for the environment and may result in the case of foods, in health hazards. Reducing returns and improving the handling improves sustainability and is beneficial for society. Future Research: The decision support system’s underlying pricing model assumes a specific business setting. This can be extended using other pricing models and applying them in a similar fashion to the current application.

Aim/Purpose: This study examines the impact of decision-making, crisis management, and decision-making on sustainability through the mediation of open innovation in the energy sector. Background: Public companies study high-performance practices, requiring overcoming basic obstacles such as financial crises that prevent the adoption and development of sustainability programs. Methodology: Due to the COVID-19 pandemic, which has led to the closure of businesses in Iraq, a survey was distributed. To facilitate responses, free consultations were offered to help complete the questionnaire quickly. Of the 435 questionnaires answered, 397 were used for further analysis. Contribution: The impact of crises that impede the energy sector from adopting sustainable environmental regulations is investigated in this study. Its identification of specific constraints to open innovation leads to the effectiveness of adopting environmentally friendly policies and reaching high levels of sustainable performance. Findings: The impacts of risk-taking, crisis management, and decision-making on sustainability have been explored. Results show that open innovation fully mediates the relationship between the factors of risk-taking, crisis management, decision-making, and sustainability. Recommendations for Practitioners: The proposed model can be used by practitioners to develop and improve sustainable innovation practices and achieve superior performance. Recommendation for Researchers: Researchers are recommended to conduct in-depth studies of the phenomenon based on theoretical and empirical foundations, especially in light of the relationship between crisis management, decision-making, and risk-taking and their impact on sustainability based on linear and non-compensatory relationships. Impact on Society: This study provides a reference for organizations with similar cultural backgrounds in adopting sustainable practices to minimize pollution in the Iraqi context. Future Research: A more in-depth study can be performed using a larger sample, which not only includes the energy industry but also other industries.

Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: This research aims to design a website-based group decision support system (DSS) user interface to support an integrated and sustainable waste management plan in Jagatera. The main focus of this research is to design a group DSS to help Jagatera prioritize several waste alternatives to be managed so that Jagatera can make the right decisions to serve the community. Background: The Indonesian government and various stakeholders are trying to solve the waste problem. Jagatera, as a waste recycling company, plays a role as a stakeholder in managing waste. In 2024, Jagatera plans to accept all waste types, which impacts the possibility of increasing waste management costs. If Jagatera does not have a waste management plan, this will impact reducing waste management services in the community. To solve this problem, the group DSS assists Jagatera in prioritizing waste based on aspects of waste management cost. Methodology: Jagatera, an Indonesian waste recycling company, is implementing a group DSS using the soft system methodology (SSM) method. The SSM process involves seven stages, including problem identification, problem explanation using rich pictures, system design, conceptual model design, real-life comparison, changes, and improvement steps. The final result is a prototype user interface design addressing the relationship between actors and the group DSS. The analytical hierarchy process (AHP) method prioritized waste based on management costs. This research obtained primary data from interviews with Jagatera management, a literature review regarding the group DSS, and questionnaires to determine the type of waste and evaluate user interface design. Contribution: This research focuses on determining waste handling priorities based on their management. It contributes the DSS, which uses a decision-making approach based on management groups developed using the SSM and AHP methods focused on waste management decisions. It also contributes to the availability of a user interface design from the DSS group that explains the interactions between actors. The implications of the availability of DSS groups in waste recycling companies can help management understand waste prioritization problems in a structured manner, increase decision-making efficiency, and impact better-quality waste management. Combining qualitative approaches from SSM to comprehend issues from different actor perspectives and AHP to assist quantitative methods in prioritizing decisions can yield theoretical implications when using the SSM and AHP methods together. Findings: This research produces a website-based group DSS user interface design that can facilitate decision-making using AHP techniques. The user interface design from the DSS group was developed using the SSM approach to identify complex problems at waste recycling companies in Indonesia. This study also evaluated the group DSS user interface design, which resulted in a score of 91.67%. This value means that the user interface design has met user expectations, which include functional, appearance, and comfort needs. These results also show that group DSS can enhance waste recycling companies’ decision-making process. The results of the AHP technique using all waste process information show that furniture waste, according to the CEO, is given more priority, and textile waste, according to the Managing Director. Group DSS developed using the AHP method allows user actors to provide decisions based on their perspectives and authority. Recommendations for Practitioners: This research shows that the availability of a group DSS is one of the digital transformation efforts that waste recycling companies can carry out to support the determination of a sustainable waste management plan. Managers benefit from DSS groups by providing a digital decision-making process to determine which types of waste should be prioritized based on management costs. Timely and complete information in the group DSS is helpful in the decision-making process and increases organizational knowledge based on the chosen strategy. Recommendation for Researchers: Developing a group DSS for waste recycling companies can encourage strategic decision-making processes. This research integrates SSM and AHP to support a comprehensive group DSS because SSM encourages a deeper and more detailed understanding of waste recycling companies with complex problems. At the same time, AHP provides a structured approach for recycling companies to make decisions. The group DSS that will be developed can be used to identify other more relevant criteria, such as environmental impact, waste management regulations, and technological capabilities. Apart from more varied criteria, the group DSS can be encouraged to provide various alternatives such as waste paper, metal, or glass. In addition to evaluating the group DSS’s user interface design, waste recycling companies need to consider training or support for users to increase system adoption. Impact on Society: The waste problem requires the role of various stakeholders, one of which is a waste recycling company. The availability of a group DSS design can guide waste recycling companies in providing efficient and effective services so that they can respond more quickly to the waste management needs of the community. The community also gets transparent information regarding their waste management. The impact of good group DSS is reducing the amount of waste in society. Future Research: Future research could identify various other types of waste used as alternatives in the decision-making process to illustrate the complexity of the prioritization process. Future research could also identify other criteria, such as environmental impact, social aspects of community involvement, or policy compliance. Future research could involve decision-makers from other parties, such as the government, who play an essential role in the waste industry.

Aim/Purpose: The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background: This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology: The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution: The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings: Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners: Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendation for Researchers: Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society: The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research: The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework.

Aim/Purpose: This study investigates the relationship between Electronic Word-of-Mouth (EWOM), Information Adoption, and the stock investment of Vietnamese investors. Background: Misinformation spreads online, and a lack of strong information analysis skills can lead Vietnamese investors to make poor stock choices. By understanding how online conversations and information processing influence investment decisions, this research can help investors avoid these pitfalls. Methodology: This study applies Structural Equation Modelling (SEM) to investigate how non-professional investors react to online information and which information factors influence their investment decisions. The final sample includes 512 investors from 18 to 65 years old from various professional backgrounds (including finance, technology, education, etc.). We conducted a combined online and offline survey using a convenience sampling method from August to November 2023. Contribution: This study contributes to the growing literature on Electronic Word-of-Mouth (EWOM) and its impact on investment decisions. While prior research has explored EWOM in various contexts, we focus on Vietnamese investors, which can offer valuable insights into its role within a developing nation’s stock market. Investors, particularly those who are new or less experienced, are often susceptible to the influence of EWOM. By examining EWOM’s influence in Vietnam, this study sheds light on a crucial factor impacting investment behavior in this emerging market. Findings: The results show that EWOM has a moderate impact on the Information Adoption and investment decisions of Vietnamese stock investors. Information Quality (QL) is the factor that has the strongest impact on Information Adoption (IA), followed by Information Credibility (IC) and Attitude Towards Information (AT). Needs for Information (NI) only have a small impact on Information Adoption (IA). Finally, Information Adoption (IA) has a limited influence on investor decisions in stock investment. We also find that investors need to verify information through official sites before making investment decisions based on posts in social media groups. Recommendations for Practitioners: The findings suggest that state management and media agencies need to coordinate to improve the quality of EWOM information to protect investors and promote the healthy development of the stock market. Social media platform managers need to moderate content, remove false information, prioritize displaying authentic information, cooperate with experts, provide complete information, and personalize the experience to enhance investor trust and positive attitude. Securities companies need to provide complete, accurate, and updated information about the market and investment products. They can enhance investor trust and positive attitude by developing news channels, interacting with investors, and providing auxiliary services. Listed companies need to take the initiative to improve the quality of information disclosure and ensure clarity, comprehensibility, and regular updates. Use diverse communication channels and improve corporate governance capacity to increase investor trust and positive attitude. Investors need to seek information from reliable sources, compare information from multiple sources, and carefully check the source and author of the information. They should improve their investment knowledge and skills, consult experts, define investment goals, and build a suitable investment portfolio. Recommendation for Researchers: This study synthesized previous research on EWOM, but there is still a gap in the field of securities because each nation has its laws, regulations, and policies. The relationships between the factors in the model are not yet clear, and there is a need to develop a model with more interactive factors. The research results need to be further verified, and more research can be conducted on the influence of investor psychology, investment experience, etc. Impact on Society: This study finds that online word-of-mouth (EWOM) can influence Vietnamese investors’ stock decisions, but information quality is more important. Policymakers should regulate EWOM accuracy, fund managers should use social media to reach investors, and investors should diversify their information sources. Future Research: This study focuses solely on the stock market, while individual investors in Vietnam may engage in various other investment forms such as gold, real estate, or cryptocurrencies. Therefore, future research could expand the scope to include other investment types to gain a more comprehensive understanding of how individual investors in Vietnam utilize electronic word-of-mouth (EWOM) and adopt information in their investment decision-making process. Furthermore, while these findings may apply to other emerging markets with similar levels of financial literacy as Vietnam, they may not fully extend to countries with higher financial literacy rates. Hence, further studies could be conducted in developed countries to examine the generalizability of these findings. Finally, future research could see how EWOM’s impact changes over a longer period. Additionally, a more nuanced understanding of the information adoption process could be achieved by developing a research model with additional factors.

Aim/Purpose: This study explored teachers’ data-driven decision-making processes during routine and emergency remote teaching, as experienced during the COVID-19 pandemic. Background: Decision-making is essential in teaching, with informed decisions promoting student learning and teachers’ professional development most effectively. However, obstacles to the use of data have been identified in many studies. Methodology: Using a qualitative methodology (N=20), we studied how teachers make decisions, what data is available, and what data they would like to have to improve their decision-making. We used an inductive approach (bottom-up), utilizing teachers’ statements related to decision-making as the unit of analysis. Contribution: Our findings shed an important light on teachers’ Data-Driven Decision-Making (DDDM), highlighting the differences between routine and Emergency Remote Teaching (ERT). Findings: Overall, we found that teachers make teaching decisions in three main areas: pedagogy, discipline-related issues, and appearance and behavior. They shift between making decisions based on data and making decisions based on intuition. Academic-related decisions are the most prominent in routine teaching, and during ERT, they were almost the only area in which teachers’ decisions were made. Teachers reported collecting data about students’ academic achievements and emotional state and considered the organizational culture, consultation with colleagues, and parents’ involvement before decision-making. Recommendations for Practitioners: Promote a culture of data-driven decision-making across the education system; Make diverse and rich data of different types accessible to teachers; Increase professional and emotional support for teachers. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research using other methodologies and in different countries. Impact on Society: This study highlights the importance of teachers’ data-driven decision-making in improving teaching practices and promoting students’ achievement. Future Research: Additional research is required to examine data-driven decision-making in diverse circumstances.

Aim/Purpose: To develop a framework for utilizing Mobile Assisted Language Learning (MALL) to assist non-native English migrant women to acquire English vocabulary in a non-formal learning setting. Background: The women in this study migrated to Australia with varied backgrounds including voluntary or forced migration, very low to high levels of their first language (L1), low proficiency in English, and isolated fulltime stay-at-home mothers. Methodology: A case study method using semi-structured interviews and observations was used. Six migrant women learners attended a minimum of five non-MALL sessions and three participants continued on and attended a minimum of five MALL sessions. Participants were interviewed pre- and post-sessions. Data were analysed thematically. Contribution: The MALL framework is capable of enriching migrant women’s learning experience and vocabulary acquisition. Findings: Vocabulary acquisition occurred in women from both non-MALL and MALL environment; however, the MALL environment provided significantly enriched vocabulary learning experience. Future Research: A standardised approach to measure the effectiveness of MALL for vocabulary acquisition among migrant women in non-formal setting

This paper discusses applications of the measures of the risk of misinforming and the role of the warranty of misinforming in the context of the informing component of Customer Related Management (CRM) issues. This study consists of two parts. Firstly, we propose an approach for customers’ grouping based on their attitude toward assessing product's properties and their expertise on the terminology/domain of the seller’s message describing the product. Also we discuss what the most appropriate personal/group warranty is for each of these group/clusters. 

The article presents Personal Knowledge Management (PKM) as an overdue individualized as well as a collaborative approach for knowledge workers. Designing a PKM-supporting system, however, resembles a so-called “wicked” problem (ill-defined; incomplete, contradictory, changing requirements, complex interdependencies) where the information needed to understand the challenges depends on upon one’s idea for solving them. Accordingly, three main areas are attended to. Firstly, in dealing with a range of growing complexities, the notion of Popper’s Worlds is applied as three distinct spheres of reality and further expanded into six digital ecosystems (technologies, extelligence, society, knowledge worker, institutions, and ideosphere) that not only form the basis for the PKM System Concept named ‘Knowcations’ but also form a closely related Personal Knowledge Management for Development (PKM4D) framework detailed in a separate dedicated paper. Reflecting back on a United Nations scenario of knowledge mass production (KMP) over time, the complexities closely related to the digital ecosystems and the inherent risks of today’s accelerating attention-consuming over-abundance of redundant information are scrutinized, concluding in a chain of meta-arguments favoring the idea of the PKM concept and system put forward. Secondly, in light of the digital ecosystems and complexities introduced, the findings of a prior article are further refined in order to assess the PKM concept and system as a potential General-Purpose-Technology. Thirdly, the development process and resulting prototype are verified against accepted general design science research (DSR) guidelines. DSR aims at creating innovative IT artifacts (that extend human and social capabilities and meet desired outcomes) and at validating design processes (as evidence of their relevance, utility, rigor, resonance, and publishability). Together with the incorporated references to around thirty prior publications covering technical and methodological details, a kind of ‘Long Discussion Case’ emerges aiming to potentially assist IT researchers and entrepreneurs engaged in similar projects.

The following definition of “option” is given in Wikipedia - “In finance, an option is a contract, which gives the buyer (the owner or holder) the right, but not the obligation, to buy or sell an underlying asset or instrument at a specified strike price on or before a specified date, depending on the form of the option” (“Option,” n.d.). Option as a risk management (mitigation) tool is broadly used in finance and trade. At the same time, it introduces asymmetry in the sense that, probabilistically, it limits the level of losses (e.g., the price of the option) and allows for unlimited gains. In the market of sophisticated devices (as smart phones, tablets, etc.), where technologies are rapidly advancing, customers usually do not have the experience to use all features of the device at the time of the purchase. Due to the lack of appropriate expertise, the risk of misinforming, leading to not purchasing the “right” device is high, but given enough time to learn the capabilities of the device and map these to the needs and tasks that device will be used for, could provide the client with substantial long term benefits. Warranty of misinforming is a mechanism that provides the client with the opportunity to explore the device and master its features under limited risk of financial losses. Thus, the warranty of misinforming could be considered as an option - the custom-ers buy it (at a fixed cost) and may gain (theoretically) unlimited benefit by realizing (within the terms of the warranty) that the device can be used to solve a variety of problems not envisaged at the time of purchase. In this study we present the idea of treating the warranty of misinforming as an option in finances and provide examples to illustrate our viewpoint.

Aim/Purpose: Personal Knowledge Management (PKM) has been envisaged as a crucial tool for the growing creative class of knowledge workers, but adequate technological solutions have not been forthcoming. Background: Based on former affordance-related publications (primarily concerned with communication, community-building, collaboration, and social knowledge sharing), the common and differing narratives in relation to PKM are investigated in order to suggest further PKM capabilities and affordances in need to be conferred. Methodology: The paper follows up on a series of the author’s PKM-related publications, firmly rooted in design science research (DSR) methods and aimed at creating an innovative PKM concept and prototype system. Contribution: The affordances presented offer PKM system users the means to retain and build upon knowledge acquired in order to sustain personal growth and facilitate productive collaborations between fellow learners and/or professional acquaintances. Findings: The results call for an extension of Nonaka’s SECI model and ‘ba’ concept and provide arguments for and evidence supporting the claims that the PKM concept and system is able to facilitate better knowledge traceability and KM practices. Recommendations and Impact on Society: Together with the prior publications, the paper points to current KM shortcomings and presents a novel trans-disciplinary approach offering appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship. Its potential to tackle opportunity divides has been addressed via a PKM for Development (PKM4D) Framework. Future DSR Activities: After completing the test phase of the prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

Aim/Purpose: This paper describes an empirical examination of how users’ willingness to disclose personal data is influenced by the amount of information provided on landing pages – standalone web pages created explicitly for marketing or advertising campaigns. Background: Provision of information is a central construct in the IS discipline. Content is a term commonly used to describe the information made available by a website or other electronic medium. A pertinent debate among scholars and practitioners relate to the behavioral impact of content volume: Specifically, does a greater amount of information elicit engagement and compliance, or the other way around? Methodology: A series of large-scale web experiments (n= 535 and n= 27,900) were conducted employing a between-subjects design and A/B testing. Two variants of landing pages, long and short, were created based on relevant behavioral theories. Both variants included an identical form to collect users’ information, but different amounts of provided content. User traffic was generated using Google AdWords and randomized between the page using Unbounce.com. Relevant usage metrics, such as response rate (called “conversion rate”), location, and visit time were recorded. Contribution: This research contributes to the body of knowledge on information provision and its effectiveness and carries practical and theoretical implications to practitioners and scholars in Information Systems, Informing Science, Communications, Digital Marketing, and related fields. Findings: Analyses of results show that the shorter landing pages had significantly higher conversion rates across all locations and times. Findings demonstrate a negative correlation between the content amount and consumer behavior, suggesting that users who had less information were more inclined to provide their data. Recommendations for Practitioners: At a practical level, results can empirically support business practices, design considerations, and content strategy by informing practitioners on the role of content in online commerce. Recommendation for Researchers: Findings suggest that the amount of content plays a significant role in online decision making and effective informing. They also contradict prior research on trust, persuasion, and security. This study advances research on the paradoxical relationship between the increased level of information and online decision-making and indicates that contrary to earlier work, not all persuasion theories‎ are ‎effective online. Impact on Society: Understanding how information drives behavior has implications in many domains (civic engagement, health, education, and more). This has relevance to system design and public communication in both online and offline contexts. Future Research: Using this research as a starting point, future research can examine the impact of content in other contexts, as well as other behavioral drivers (such as demographic data). This can lead to theoretical, methodological, and practical recommendations.

Aim/Purpose: This article takes ‘tourism decision-making behavior’ as an entry point, and deeply analyzes the factors influencing the travel decision-making of Chinese ‘American Travel’ tourists and their degree of influence, so as to provide a reference for the development of Chinese outbound tourism. Background: With the development of China’s economy and the improvement in people’s level, the outbound tourism market of Chinese residents has developed rapidly. The United States has become an important tourism destination country for Chinese residents’ outbound tourism, and China has also become one of the important tourist source countries of American tourism. However, the rapid development of ‘American tourism’ has also caused competition problems in China’s tourism industry. For example, prices and tourism products have become a means of competition among tourism enterprises. As the main body of consumption, tourists’ decision-making behavior will be affected by various factors. Methodology: Drawing lessons from previous scholars’ research results on tourism decision-making behavior, the influencing factors of tourism decision-making behavior are summarized. A theoretical model and index system of factors influencing tourism decision-making behavior of Chinese residents ‘Travel in the United States’ are established, research hypotheses are put forward, questionnaire data are collected, and SPSS and Amos are used to analyze and verify the theoretical model. Contribution: This research expands the literature on topics related to tourism decision-making in research and practice. It establishes a theoretical model and index system for the factors that influence the decision-making behavior of Chinese residents’ ‘American Travel’ tourism. In addition, we propose countermeasures for tourism products, enterprises, and the government. Findings: Prior knowledge and external information have a positive influence on tourism perception and value perception, and a negative influence on risk perception. Risk perception value perception has a positive and negative influence on tourism decision-making and tourism motivation, respectively. Tourism motivation has a positive influence on tourism decision-making and has a positive impact. Recommendation for Researchers: According to the research conclusions of this article, the following counter-measures and suggestions are put forward from three aspects of tourism: products, enterprises, and governments. On the basis of existing tourism products, relevant operating companies should pay more attention to the upgrading and transformation of tourism, leisure and entertainment products in scenic spots to increase the willingness of tourists to travel. When considering corporate marketing and promotion plans, tourism companies operating related businesses should increase the weight of their marketing budgets in online marketing, increase investment in online marketing, and develop mobile applications that meet the preferences of Chinese residents in the United States. Do a good job in the timely publication of safety reminders and local information. Safety is an important foundation for tourism development and the core concern of many tourists. Future Research: Due to the important research on the impact of tourism activities, the influencing factors are many and complex, and the psychological process of tourism decision-making is carried out directly. There are still unconsidered factors that need to be studied in depth. In the future, it is possible to compare multiple resource-featured themes, and increase the characteristics of potential tourists, and the factors affecting the selection behavior of regional cultural tourists, and so forth, in order to make the research more applicable and practical instructive significance.

Aim/Purpose: Explores the interrelationships between uncertainty, motivation, and IT readiness when predicting IOS adoption among small businesses. Background: Small business IOS adoption is proportionally low in most countries worldwide. Methodology: Uses a sample of small businesses and PLS structural-equations path modelling approach. Contribution: Uncertainty is an underexplored construct in information systems research, and our research shows that it plays a significant role in IOS adoption among small businesses Findings: The findings support that uncertainty has a negative effect on intent to adopt IOS and that motivation and IT readiness have a positive effect. Recommendation for Researchers: To alleviate uncertainty, an effort to win over small business managers to IOS over the internet must encompass accessible information, security provisions, low-cost product, simple interfaces, and system adaptability to existing provisions in the IOS network. The uncertainty perspective has not been tested extensively empirically, especially not in the context of technology adoption, and needs further investigation. Future Research: Future research could explore the uncertainty construct in the context of IOS among different size businesses

Aim/Purpose: The aim of the present paper is to present a Noise Decision (ND) scale. First, it reports the development and validation of the instrument aimed at examining organizational factors that have an influence on decision-making and the level of noise. Second, it validates this rating scale by testing its discriminant and convergent validity with other measures to assess decision-making qualities. Background: According to the literature, the concept of noise is the unwanted variability present in judgments. The notion of noise concerns the systematic influence to which individuals are exposed in their environment. The literature in the field has found that noise reduction improves the perception of work performance. Methodology: The first study involves the development of a scale (composed of 36 items) consisting of semi-structured interviews, item development, and principal component analysis. The second study involves validation and convergent validity of this scale. In the first study, there were 43 employees from three medium-sized Italian multinationals. For the second study, a sample of 867 subjects was analysed. Contribution: This paper introduces the first scale aimed at assessing noise within individuals and, in the organizational context, within employees and employers. Findings: Results show that the estimated internal reliability for each of the ND subscales and also the correlations between the subscales were relatively low, suggesting that ND correctly measures the analyzed components. Furthermore, the validation of the psychometric qualities of the ND allowed for the assertion that the influence of noise is present in the decision-making process within the context of work environments, validating the initial hypotheses. Recommendation for Researchers: This paper aims to improve theory and research on decision-making; for example, by providing a possible implementation for scales for evaluating decision-making skills. Furthermore, detecting and limiting noise with a systematic method could improve both the quality of decisions and the quality of thought processes. Future Research: Given the measurement of ND, the study can be a starting point for future research on this topic. Since there is no literature about this construct, it would be necessary to spend more time researching, so that the topic becomes clearer. System noise has been tested by some researchers with a “noise audit,” which means giving the same problem to different people and measuring the differences in their responses. Repeating this kind of audit in conjunction with the ND in a specific work environment could be helpful to detect but also measure the influence of noise.

Aim/Purpose: The paper discusses various factors contributing to disagreements, such as differing experiences, perspectives, and historical narratives, leading to disagreements within families and societies. It explores how beliefs, values, and biases feed into disagreements, with confirmation bias affecting decision-making and the media. Cultural values also play a role, showcasing conflicts between meritocracy and inclusivity in ethical decision-making. Haidt's Moral Foundations Theory highlights differences in value priorities between Western and Eastern societies. The impact of Western values like rationalism, freedom, and tolerance, under threat from Marxist illiberalism on campuses, is dis-cussed. The text also delves into disinformation, emotions in warfare, and the use of fake information and images for propaganda purposes. The need for diligent reporting to avoid spreading disinformation is emphasized, given its potential to create misconceptions and harm diplomatic relations.

Aim/Purpose: The aim of the present study is to investigate how pre-game superstition and anxiety can drive the consumption and purchase of sports products and objects by triathletes. Methodology: We tested our hypotheses via a cross-sectional study on a sample of N=124 triathletes. Contribution: The originality of our work stands in the provision of empirical evidence on the role of superstition and anxiety in characterized consumer decision-making of triathletes. Theoretically and practically, our results can extend our knowledge of the role of cognitive factors in consumer behaviors among athletes. Findings: The results of the Structural Equation Modelling provided evidence of our hypothesized relationship between pre-game anxiety and superstition, and cognitive biases. Pre-game anxiety increases the level of incidence of specific cognitive biases characterized by intuitive and implicit thinking, while superstition leads to more rational and personal cognitive biases, which affect their purchasing of sports products before games and competitions.

Lihan TONG,Weijia LI,Qingxia YANG,Liyuan CHEN,Peng CHEN, Vol.E107-D, No.11, pp.1472-1475
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.
Publication Date: 2024/11/01

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.