Research Decisions Research Articles

The Use of Cloud Platform Technology to Help University Music Teachers Teaching Decision-Making Research

With the COVID-19 pandemic, more and more universities are turning to online teaching, and music teaching is no exception. Because music teaching pays attention to the particularness of teachers' demonstration and students' imitation, the change of teaching environment from offline teaching to online teaching has a greater impact on the music discipline, which has new requirements for teachers' decision-making level. Through literature and questionnaires, this study identified that the most significant factor affecting teachers' decision making after the teaching environment was changed to online teaching was teacher-student interaction and designed a model to improve the interactive teaching of teachers through cloud platform technology. It is found that the model can effectively improve the interaction between teachers and students, so that teachers can make good decisions more easily and easily, so as to improve students' music scores and achieve effective teaching.

Advice from artificial intelligence: a review and practical implications.

Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual "fit"-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.

Decision making methods used for improving power quality to meet the sustainable energy targets

A higher quality energy demand in line with sustainable goals has led researchers to new studies. The concept of power quality has become very popular in this direction in the last decade. Decision making concept, which is another important and popular concept, has become an indispensable tool in solving difficult decision making problems in many areas. The main starting point of this study was to unearth the crucial studies on “decision making for a better power quality,” which is at the intersection of these two important issues. In this direction, the deficiency in the literature was noticed, the related studies were examined, and the methodological features and prominent aspects of the studies in different categories were examined. The active use of many decision-making methods in the search for solutions to different power quality problems has been demonstrated. The conclusion that decision-making mechanisms offer effective solutions in solving power quality problems has been more clearly emphasized in this study. This review furnishes researchers with crucial insights into current trends in power quality and multi-criteria decision-making research. It also delineates potential avenues for additional research in this increasingly popular field.

A Generalized Method for Deriving Steady-State Behavior of Consistent Fuzzy Priority for Interdependent Criteria

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach’s real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP’s network structure with the fuzzy DEMATEL’s influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM).

Artificial Intelligence and Blockchain Technology Drive Leadership Decision-Making Research Group Recommendation Algorithm

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members’ influence and leader impact. In this paper, a model has been proposed to compute members’ influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders’ judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms.

Leveraging the interplay of Internet of Things (IoT) capabilities for achieving customer satisfaction in a B2B2C context: an integrative perspective

PurposeDespite IoT’s huge potential, enterprises’ ability to leverage it is their competitive advantage. Thus, competitive differentiation is primarily predicated on leveraging IoT toward customer needs. To examine the research gap, this study aims to explore the drivers of customer satisfaction and how they are affected by the interaction between IoT capabilities.Design/methodology/approachA mixed-method research framework is applied to assess the impact of IoT capabilities on customer satisfaction. Based on the theoretical underpinning of the resource-based view and dynamic capability, the study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. First, DEMATEL is used capture the interrelationship between IoT capabilities. Further, the impact of each IoT capabilities on customer satisfaction is studied using CoCoSo method.FindingsThe study highlights the importance of IoT capabilities in active resource allocation and effective resource utilization. The findings are enriched through the complementarity of resources in a dynamic business-to-business-to-customer (B2B2C) scenario. The authors expand the IoT capabilities from conventional business-to-business (B2B) or business-to-customer (B2C) scenario to tri-nodal B2B2C relationship triangle.Practical implicationsBased on the findings, the authors offer a business transformation strategy for firms in key areas of customer satisfaction by leveraging IoT. The study can help management prioritize and develop key IoT capabilities to meaningfully increase customer satisfaction metrics.Originality/valueBuilding on the dynamic capabilities and resource-based view of the firm, an integrated decision-making research model is proposed. In addition, this study investigates the product and service capabilities unlocked using IoT capabilities. This work can be considered one of the leading attempts to improve customer satisfaction using IoT capabilities from traditional dyadic (B2B or B2C) structure to triadic (B2B2C) framework.

EEMD-ARIMA Model for annual precipitation forecasting to aid weather insurance decision-making research

Climate risk poses significant threats to human life and property. Climate insurance can effectively mitigate and disperse these risks. This paper addresses the weakness in weather risk prediction for climate insurance formulation by combining Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive Integrated Moving Average (ARIMA) models. Four models, namely EMD, EEMD, ARIMA, and EMD-ARIMA, were established for modeling and forecasting Chinas annual precipitation data. The results show that the EEMD-ARIMA model can suppress the modal aliasing problem in time series and has the best fit compared to other models. This model can more accurately describe the variation in annual precipitation in forecasting applications, providing significant predictive value for insurance companies and government decisions regarding insurance and climate risk management.

A multiple attribute group decision making model based on 2-tuple linguistic pythagorean fuzzy dombi aggregation operators for optimal selection of potential global suppliers

Multiple-Attribute Group Decision-Making (MAGDM) is a significant area of research in decision-making, and its principles and methodologies are widely implemented. A Pythagorean Fuzzy Set (PFS) is an extension of an Intuitionistic Fuzzy Set (IFS) that is highly valuable for representing uncertain information in real-world scenarios. The 2-Tuple Linguistic Pythagorean Fuzzy Number (2TLPFN) is a specific type of Pythagorean Fuzzy Number (PFN) that can be used to represent uncertainty in real-world decision making through the use of 2-Tuple Linguistic Terms (2TLTs). This paper focuses on the examination of Multiple Attribute Group Decision Making (MAGDM) using 2TLPFNs. Dombi's t-norm and t-conorm operations were commonly referred to as Dombi operations, which might have been greater degree of applicability if offered in a new form of flexibility within the general parameter. In this research, we implement Dombi operations to construct some 2-Tuple Linguistic Pythagorean Fuzzy (2TLPF) Dombi Aggregation operators. These operators include the 2TLPF Dombi Weighted Averaging (2TLPFDWA) operator, 2TLPF Dombi Ordered Weighted Averaging (2TLPFDOWA) operator, 2TLPF Dombi Weighted Geometric (2TLPFDWG) operator, and 2TLPF Dombi Ordered Weighted Geometric (2TLPFDOWA) operator. An analysis is conducted to examine the unique characteristics of these suggested operators. Subsequently, we leveraged the proposed operators to develop a model aimed at tackling the MAGDM problems in the 2TLPF environment. Eventually, a suitable instance has been demonstrated to validate the formation of the model as well as exhibit its implementation and resilience.

TRENDS AND PATTERNS IN ONLINE PURCHASE DECISION RESEARCH A LITERATURE REVIEW AND BIBLIOMETRIC ANALYSIS

Abstract : The internet has transformed the way consumers make purchase decisions, and with the increasing popularity of e-commerce, it has become vital to understand the factors that influence online purchase decisions. The COVID-19 pandemic has further accelerated the shift towards online shopping, making it even more crucial to understand the online purchase decision-making process. This bibliometric review examines 95 articles on the keyword "online purchase decision" from Scopus journals generated by Publish or Perish on April 25th, 2023, to identify the most influential authors and trends in this field from 2002 to 2023. The study reveals a steady rise in publications on this subject over time, with a significant surge in recent times. S. Yoon emerged as the most cited author in this field, followed by H.W. Kim and N. Hu. "Decision Support Systems" is the most productive journal in this field, and M. Ramesh is the most prolific author. The research variables have similarities and differences in focus and approach. For instance, trust, price, reviews, website characteristics, and the influence of social media are the focuses in these studies. The studies differ in their approaches and focuses. For example, (Yoon, 2002) examines the antecedents and consequences of trust, (Kim et al., 2012) compares price and trust, (Hu et al., 2014) analyzes online review sentiment, and (Luo et al., 2012) investigates the impact of website characteristics. There are also differences in the scope of the research, including cross-cultural approaches, the teenage market, gender, and potential customers vs. repeat customers. The findings of this study provide valuable insights into the key trends and patterns in online purchase decision-making research, which can inform marketing strategies and improve online salesKeyword : Keputusan Pembelian Online, Analisis Bibliometrik, Perilaku Konsumen

A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping

System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.

Flood Susceptibility Mapping for Kedah State, Malaysia: Geographics Information System-Based Machine Learning Approach

ABSTRACT Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. Aim: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influencing factors from a geospatial database were taken into account when mapping the flood-prone areas. Every element demonstrated a robust relationship with the probability of flooding. Results: The highest contributing elements for the flood disaster in the study region were drainage density, distance, and the curvature. Flood susceptibility models’ performance was validated using standard statistical measures and AUC. The ROC curves demonstrated that all ensemble models had good performance on the validation data sets (AUC = >0.97) with high accuracy scores of 0.80. Based on the flood susceptibility maps, most of the northwest regions of the study area are more likely to flood because of low land areas, areas with a lower gradient slope, linear and concave shape curvature, high drainage density with high rainfall, more “water bodies,” “crops land,” and “built areas,” abundance on sea and surface water, and Quaternary types of soil feature and so on. The very high flood susceptibility class accounts for 18.2% of the study area, according to the RF-embedding model, whereas the high, moderate, low, and very low susceptibility classes were found at about 20.0%, 24.6%, 24.3%, and 12.9%, respectively. Conclusion: In comparison with other commonly used applied approaches, this research presents a novel modeling approach for flood susceptibility that integrates machine learning and geospatial data. It has been found to be stronger and more efficient, highly accurate, has good prediction performance, and is less biased. Overall, our research into machine learning-based solutions points in a positive path technologically and can serve as a reference manual for future research and applications for academic specialists and decision-makers.

Análise Bibliométrica de Publicações Científicas sobre Visualização de Dados e Tomada de Decisão

Decision-making and data visualization play crucial roles in both business and social environments, where efficient use of information is essential. This article proposes a bibliometric study to analyze the evolution of the thematic elements of decision-making and data visualization, with an emphasis on Information Management. Using statistical methods, the research adopts an exploratory-descriptive approach, employing bibliometric techniques and exploring the Scopus database from 1987 to 2023. Analyses revealed a remarkable growth in publications related to the subject, with an increase of over 80% compared to the early years of the decade. Furthermore, citations per published article surpassed the sum of all previous years before 2008, indicating a clear growth trend in the field. This phenomenon reflects the emergence of new research and works focused on the integration of new technologies and their application in generating practical information for organizational use. Countries such as the United States, China, Germany, and Denmark stand out as major knowledge generators in this domain, although there is an increase in cooperation between countries and their respective authors. Author analysis reveals significant growth in collaboration, evidenced by multiple works published by the same authors and co-authors. This study contributes to understanding the evolution of research in decision-making and data visualization, providing insights into current and future trends in this dynamic field.

The Causes and Consequences of Drifting Expectations

Awaiting news of uncertain outcomes is distressing because the news might be disappointing. To prevent such disappointments, people often “brace for the worst,” pessimistically lowering expectations before news arrives to decrease the possibility of surprising disappointment (a negative prediction error, or PE). Computational decision-making research commonly assumes that expectations do not drift within trials, yet it is unclear whether expectations pessimistically drift in real-world, high-stakes settings, what factors influence expectation drift, and whether it effectively buffers emotional responses to goal-relevant outcomes. Moreover, individuals learn from PEs to accurately anticipate future outcomes, but it is unknown whether expectation drift also impedes PE-based learning. In a sample of students awaiting exam grades (N = 625), we found that expectations often drift and tend to drift pessimistically. We demonstrate that bracing is preferentially modulated by uncertainty; it transiently buffers the initial emotional impact of negative PEs but impairs PE-based learning, counterintuitively sustaining uncertainty into the future.

Affective prediction errors in persistence and escalation of aggression.

People generally empathize with others and find harm aversive. Yet aggression, for example, between groups, abounds. How do people learn to overcome this aversion in order to aggress? Many models of learning emphasize outcome prediction errors-deviations from expected outcomes in the environment-but aggression may also be fueled by affective prediction errors (affective PEs)-deviations from how we expect to feel. Across five preregistered online experiments that hold outcome prediction errors constant (N = 4,607), participants choosing aggressive or nonaggressive actions aggressed more against disliked group members and often escalated or persisted in taking actions that felt better than expected (positive affective PE), especially when those actions were aggressive. Crucially, inducing incidental empathy toward the group of the target rendered affective PE signals sensitive to group identification-participants escalated aggression that felt better than expected relatively less toward liked versus disliked group members. That said, affective PEs did not always add explanatory power beyond levels of postoutcome affect alone; we discuss the importance and implications of these results. In summary, we reveal affective PE integration as a candidate algorithm facilitating exceptions to harm aversion in intergroup conflict. More broadly, we highlight for affective science and decision-making researchers the necessity of appropriately testing separable components of affective signals in predicting subsequent behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Use of Research in Schools: Principals’ Capacity and Contributions

Policy expectations for the role of research evidence in educational decision-making have grown exponentially in the U.S. and globally, yet there has been limited attention to school capacity to engage in evidence-informed improvement. In this paper, I address this gap by first conceptualizing principal leadership for evidence use and, second, use this conceptual lens to examine large-scale survey data about school evidence use practices and capacity. Drawing on data from a national survey of more than 4000 educators in 134 schools in the US, I explore school practices and capacity to use research and surface opportunities and needs for principal leadership in evidence-informed improvement. Findings suggest that there is an opportunity to improve the role of research in school improvement decision-making, and that principals may contribute to school capacity in specific ways that relate to developing human capital, influencing culture, leveraging resources, and shaping decision-making. Data reveal moderate evidence of research use in agenda setting and of organizational routines that support research use, but lack of uptake of those routines for research use as well as limited investment in resources (e.g., time). Further, decision-making was distributed across a wide range of improvement initiatives, with evidence of a lack of clarity about goals. Although principals report confidence and experience with using research, overall school staff also reported limited experience with prior research, including coursework or participation in research, and low confidence in critically consuming research. Implications point to the need to strengthen principals’ own evidence use capacity as well as focus on dimensions school capacity as part of evidence use initiatives. Recommendations suggest strategies for developing principals’ knowledge and skills around leadership for evidence-informed improvement.

A scientometric analysis of multicriteria decision-making research

ABSTRACT Multi-criteria decision-making (MCDM) is an important decision-making technique for incorporation into Decision Support Systems (DSS). This paper uses bibliographic techniques to characterise the MCDM research field. This research identifies the historical origin of MCDM in both Operations Research/Management Science (OR/MS) and Computer Science. This research identifies the increasing importance of environmental and medical applications in addition to traditional engineering and business applications. It also examines the core literature and the extent to which newer papers are cited.

When We Are Happy, We Are the Same—Emotions as a Boundary Condition for the Impact of Cultural Differences on Strategic Decisions

Strategic decision-making research has mainly relied on the values-based approach to culture. However, the dynamic constructivist approach to culture has shown that cultural tendencies may also be altered by contingency factors in the decision-making process itself. We theorize based on the appraisal tendency framework as well as the concept of cultural affordances that emotions, such as happiness, can alter the cultural dispositions of managers from Western and East Asian contexts. To test our hypotheses, we conduct a lab-in-the-field experiment with 187 executives from China and Germany, and measure emotions based on participants’ psychophysiological skin conductance responses. Our results show that happiness moderates and can even reverse initial cultural dispositions in executives’ strategic decision-making behavior. These findings suggest that emotions may be important contingency factors that can alter the initial cultural dispositions of decision makers in the strategy process.

Bent-half space model problem for Lame equation with surface tension

The study of fluid flow is a very fascinating area of fluid dynamics. Fluid motion has received more and more attention in recent years and numerous researchers have looked into this topic. However, they rarely used a mathematical analysis approach to analyse fluid motion; instead, they used numerical analysis. This serves as a significant justification for the researcher's decision to study fluid flow from the perspective of mathematical analysis. In this paper, we consider the ${\mathcal R}$-boundedness of the solution operator families of the Lam\'e equation with surface tension in bent half-space model problem by taking into account the surface tension in a bounded domain of {\it N}-dimensional Euclidean space ($N \geq 2$). The motion of the model problem can be described by linearizing an equation system of a model problem. This research is a continuation of [13]. They investigated the ${\mathcal R}$-boundedness of the solution operator families in the half-space case for the model problem of the Lam\'e equation with surface tension. First of all, by using Laplace transformation we consider the resolvent of the model problem, then treat the problem in bent half-space case. By using Weis's operator-valued Fourier multiplier theorem, we know that ${\mathcal R}$-boundedness implies the maximal $L_p$-$L_q$ regularity for the initial boundary value. This regularity is an essential tool for the partial differential equation problem.

Advancing a theoretical framework for exploring heuristics and biases within HR decision-making contexts

PurposeThis paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.Design/methodology/approachThis paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.FindingsThe authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.Practical implicationsDecades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.Originality/valueHeuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

The Role of Marketing Research in Managerial Decision–making: A case study of Harare-based Food–Manufacturing Organisations in Zimbabwe

In recent years, a market oriented organisational culture has increasingly been considered a key element of superior organisational performance. Marketing research has an integrative marketing function of helping ensure that all departments in an organisation work together towards consumer satisfaction. The study sought to investigate the role of marketing research in managerial decision-making in Harare-based food manufacturing organisations. The investigation employed a quantitative research design. The research population comprised all marketing managers in Harare-based Food–manufacturing organisations. The population had a total of 19 organisations, 6 of which did not have marketing managers in post. This reduced the population to 13. Using a simple random sampling procedure, 10 marketing managers were randomly selected to participate in the research. Data were collected through a structured questionnaire with both open-ended and closed-ended questions, in which marketing managers indicated their views on the key roles of marketing research in managerial decision-making in Harare-based Food–manufacturing organisations. Marketing managers perceived and were in agreement that the role of providing information that can be used in facilitating making informed, quality marketing decisions was central to marketing research. Findings revealed that key roles of marketing research in managerial decision-making included roles in aiding satisfaction of consumer needs and wants; market segmentation; targeting and positioning; identifying and assessing threats from environmental forces. Given the central role of marketing and marketing research in managerial decision-making, organisations in the food-manufacturing industry are urged to adopt a market-led approach and achieve market leadership through superiority in aiding satisfaction of customer needs and wants through a co-ordinated set of activities that enhance attainment of organisational goals and strategic objectives.