Structured Perspective Research Articles

Ehsan’s Three Tables Model: A comprehensive guide for identifying research gaps and conducting systematic literature reviews in business and economics studies

The aim of this study is to introduce and assess Ehsan’s Three Tables Model as a novel framework for conducting systematic literature reviews in business and economics studies. The primary challenge in this field lies in the inconsistent identification of research gaps, which often impedes knowledge advancement. To address this, Ehsan’s Three Tables Model offers a structured and comprehensive approach designed to improve the precision and clarity of research gap identification. Methods employed include a systematic review of existing literature, deductive analysis, and the application of the Three Tables Model to categorize and analyze research gaps across various industries and regions. Results of the study demonstrate that this model offers a more rigorous methodology for organizing and analyzing literature, ensuring that identified gaps are both relevant and actionable. It moves beyond conventional approaches by employing a three-step process: compiling relevant studies, categorizing research gaps, and outlining how the current study fills these gaps. The author concluded that the proposed model contributes to the field of business and economics studies by presenting a fresh, structured perspective on literature reviews, providing researchers with an innovative tool to conduct more impactful and targeted studies.

Decoding modular reconfigurable robots: A survey on mechanisms and design

The intrinsic modularity and reconfigurability of modular reconfigurable robot (MRR) systems confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple MRR categories, each described by its respective morphology or capability characteristics, leading to some ambiguity in the taxonomy. This paper conducts a comprehensive survey covering a wide range of mechanism and design aspects of MRR systems, spanning from 1985 to 2023. It introduces an innovative, unified conceptual framework for understanding MRR hardware, which encompasses three pivotal elements: connectors, actuators, and homogeneity. Through the utilization of this trilateral framework, this paper systematically interprets and classifies MRR systems over the years, aiming to provide a structured taxonomy perspective and enhance understanding of MRR. It explains the fundamental attributes characterizing MRR systems and their compositional aspects, providing insights into their design, technology, functionality, and categorization. Augmented by the proposed trilateral framework, this paper also elaborates on the trajectory of evolution, prevailing trends, principal challenges, and potential prospects within the field of MRR.

Learning from educators: Implementation of a positive behavior support program targeting challenging behavior in children with autism

Challenging behaviors (CB) are a frequent co-occurring problem in children with autism spectrum disorder (ASD) and hinder their response to recommended interventions such as early intensive behavioral intervention (EIBI). The Prevent-Teach-Reinforce for young children (PTR-YC) program was implemented to meet community-based EIBI educators’ training and support needs in managing CB in their day-to-day work with families. Although this positive behavior support program has a strong empirical basis, its implementation by community-based educators has yet to be assessed from a systematic and structured program evaluation perspective. Using Chen’s (2015) theoretical framework for program evaluation, this study assessed the quality of implementation of PTR-YC as perceived by 17 educators who received training and supervision on applying PTR-YC among families of children with ASD receiving EIBI services. Educators’ post-intervention interviews and questionnaires were analyzed using the logical model for program evaluation to identify obstacles and facilitators to the implementation of PTR-YC.

The game management framework for sports refereeing: a structured perspective on officiating performance and its development

AbstractThe Game Management Framework for Sports Refereeing (GMFSR) provides a comprehensive and novel approach to understanding the performance of on-field interactor referees in competitive sports. It highlights key performance factors and focuses on the holistic integration of essential elements. According to the GMFSR, officiating performance is based on a referee’s embodied concepts: rules, laws and regulations and game knowledge; contextual judgment; physical fitness, positioning, and signaling; and dynamic communication and arbitration. These elements underpin the four skills of administering the game, decision-making, creating presence, and guiding the game, which together make up a referee’s game management competency. In addition, the framework emphasizes the importance of a referee mindset, stressing the influence of personal attributes and adaptive strategies on overall performance. Developed by integrating empirical evidence, different conceptual and theoretical perspectives, and expert knowledge from academia and practice, the GMFSR aims to provide a comprehensive foundation on which to stimulate ideas for the education and training of referees. It is hoped that by considering all elements of the GMFSR at the novice level and developing them throughout a referee’s career, it will provide holistic yet focused training, and serve as a valuable resource for future research.

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions.

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks.

Conceptual Framework of Factors Enhancing Sustainability-oriented Innovations

Sustainable innovations offer opportunities to address environment and social concerns along with ensuring economic gains. To harness this opportunity, organisations require relevant organisational and strategic skills. This study uses a semi-systematic literature review combined with content analysis to identify and synthesise 87 peer-reviewed research papers and create a comprehensive conceptual framework of internal firm factors that enhance sustainability-oriented product and process innovations in an organisation. It identifies 10 variables that incorporate the four broad factors required for successful sustainable innovation performance, namely, requisite resources and capabilities, conducive organisational structures and processes, a culture that promotes sustainable innovations and leadership that is committed to sustainability goals. The framework provides a comprehensive and structured perspective on the internal factors promoting sustainable innovations, and, therefore, gives practical insights to organisations in their sustainability journey.

Integrating Machine Learning with Non-Fungible Tokens

In this paper, we undertake a thorough comparative examination of data resources pertinent to Non-Fungible Tokens (NFTs) within the framework of Machine Learning (ML). The core research question of the present work is how the integration of ML techniques and NFTs manifests across various domains. Our primary contribution lies in proposing a structured perspective for this analysis, encompassing a comprehensive array of criteria that collectively span the entire spectrum of NFT-related data. To demonstrate the application of the proposed perspective, we systematically survey a selection of indicative research works, drawing insights from diverse sources. By evaluating these data resources against established criteria, we aim to provide a nuanced understanding of their respective strengths, limitations, and potential applications within the intersection of NFTs and ML.

Competition and cooperation: a coopetition strategy for sustainable performance through serial mediation of knowledge sharing and open innovation

Purpose The purpose of this study is to examine the effect of the coopetition strategy (CS) (the simultaneous pursuit of collaboration and competition) on sustainable performance (SP) through the serial mediation of knowledge sharing (KS) and open innovation (OI). Design/methodology/approach A structured questionnaire was used to gather data from corporate business enterprises, and partial least squares structural equation modeling was used for analysis. Findings Empirical evidence supports the coopetition strategy's role in enhancing KS, which in turn fosters OI, leading to improved SP. It has also been concluded that KS and OI have a significant serial mediation effect on the relationship between CS and SP. Practical implications Through the integration of KS and inward-outward open innovation, the coopetition model enables coopetitors leverage each other’s resources and capacities for mutual sustainability. To fully benefit from it, small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) must change their perception of free competition and actively engage in coopetition activities, particularly in the realms of knowledge and OI. Originality/value The most novel contribution of this study to the growing body of knowledge on SP is the establishment of empirical evidence regarding the crucial role of a serial mediation of KS and OI in the relationship between CS and SP. Unlike earlier research, this study provides a structured perspective and understanding of how and why CS, KS and OI were leveraged to enhance the SP of SMEs.

KÜLTÜREL TURIZMIN DOKUSUNU ORTAYA ÇIKARMAK: İSTANBUL'DA TURISTLER VE DENEYIMLER, MEMNUNIYET VE ETKILEŞIM ARASINDAKI BAĞLANTIYI İNCELEMEK

This study introduces a novel approach to cultural tourism in Istanbul by quantifying satisfaction and engagement as measurable values. The complex dynamics of cultural tourism in Istanbul is examined, with a specific focus on the interplay between tourist experiences, satisfaction levels, and the extent of engagement with the city’s cultural offerings. Employing a quantitative research design, the investigation surveys tourists in Istanbul and employs rigorous statistical analyses to derive meaningful findings. Drawing insights from a sample of 230 respondents, the research presents the findings based on different aspects of cultural tourism, including tourist experiences, satisfaction, and engagement. Departing from traditional qualitative assessments, our research employs regression analyses to systematically explore the relationships between tourist experiences and these quantifiable dimensions. This innovative model offers a structured perspective on the impact of tourist experiences, providing unique insights into the intricate dynamics that shape cultural tourism in Istanbul. This methodological novelty advances the empirical understanding of cultural tourism and establishes a robust foundation for future research endeavors. The character of Istanbul as a cultural tourist destination is intricately shaped by the nuanced interactions between tourists and the city’s tourism infrastructure. These interactions, characterized by a symbiotic relationship, meet travelers’ expectations, and frequently surpass them, laying the foundation for a heightened sense of happiness. Satisfaction, a pivotal component of cultural tourism, is intricately linked to the authenticity of experiences. Istanbul’s open infrastructure and unwavering commitment to preserving cultural heritage contribute significantly to the happiness and satisfaction of its visitors. Moreover, the dynamic facet of cultural tourism, termed engagement, transforms visitors into active participants in the cultural narrative. Engaged visitors seek deeper connections, fostering an upward spiral of satisfaction and positive experiences. The implications of these findings presented in the results obtained carry substantial weight for stakeholders in Istanbul’s cultural tourism landscape, offering valuable insights that can inform strategic decisions and contribute to the sustainable development of cultural tourism in the city. Keywords: cultural tourism, tourist satisfaction, Istanbul.

Brazil’s Formal E-Waste Recycling System: From Disposal to Reverse Manufacturing

This study investigates the post-regulation scenario of waste electrical and electronic equipment (e-waste) reverse logistics in Florianópolis, an island in southern Brazil, following Decree N° 10240/2020. Employing a case study approach, involving observation and semi-structured interviews, the research explores key stakeholders’ roles. The results indicate collaborative efforts among the municipal cleaning service provider, recycler, and manager entity, supporting effective e-waste take-back systems. This cooperation not only strengthens the formal recycling market but also yields socio-environmental benefits. The study reveals that optimal outcomes arise from the collaboration between the public cleaning service provider and the business sector, fostering a mutually beneficial relationship. Concerning e-waste value recovery, there is a notable inclination to extend the life cycle of small equipment, emphasizing reuse and remanufacture cycles, while larger electrical and electronic items follow to recycling and recovery cycles. In summary, this research contributes to aligning stakeholders in e-waste reverse logistics, emphasizing compliance with legislation and fostering a deeper understanding of roles, functions, and alliances. The study provides a strategic and structured perspective on e-waste management in a city renowned for selective waste collection and considered a national benchmark for reverse logistics, reinforcing its commitment to sustainability.

Deep Neural Networks Pruning via the Structured Perspective Regularization

Deep Neural Networks Pruning via the Structured Perspective Regularization

Time-series pattern recognition in Smart Manufacturing Systems: A literature review and ontology

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the acquisition of vast amounts of data from diverse sources such as machine tools, robust and affordable sensor systems with advanced information models, and other sources within Smart Manufacturing Systems (SMS). As a result, the amount of data that is available in manufacturing settings has exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and Machine Learning (ML) to be leveraged. Time-series analytics has been successfully applied in a variety of industries, and that success is now being migrated to pattern recognition applications in manufacturing to support higher quality products, zero defect manufacturing, and improved customer satisfaction. However, the diverse landscape of manufacturing presents a challenge for successfully solving problems in industry using time-series pattern recognition. The resulting research gap of understanding and applying the subject matter of time-series pattern recognition in manufacturing is a major limiting factor for adoption in industry. The purpose of this paper is to provide a structured perspective of the current state of time-series pattern recognition in manufacturing with a problem-solving focus. By using an ontology to classify and define concepts, how they are structured, their properties, the relationships between them, and considerations when applying them, this paper aims to provide practical and actionable guidelines for application and recommendations for advancing time-series analytics.

Unpacking the sustainable performance in the business ecosystem: Coopetition strategy, open innovation, and digitalization capability

This study aims to examine the mediating role of open innovation and the moderating effect of digitalization capabilities in the relationship between coopetition strategy and sustainable performance in the Belt and Road Initiative (BRI), which offers a coopetitive climate and is the most widely recognized business ecosystem. We conducted an empirical analysis using the partial least squares (PLS) structural equation model (SEM) based on 520 firm datasets from multiple hubs of BRI. The results show that open innovation partially mediates the relationship between coopetition strategy and sustainable performance. The results also indicate that digitalization capability significantly moderates the relationship between coopetition strategy and open innovation. However, there was insignificant moderating effect between coopetition strategy and sustainable performance of digitization capability. We believe that our research, which is based on the dynamic capability perspective, provides a structured perspective and understanding of how and why coopetition strategy, open innovation, and digitalization capabilities can be leveraged to achieve a firm's sustainable performance in the BRI.

Rethinking strategic sourcing during disruptions: A resilience‐driven process contribution to knowledge on supply chains

AbstractThis paper aims to present a structured perspective regarding the strategic sourcing process with the aim of enhancing supply chain resilience during disturbing events. For this purpose, it uses a descriptive approach, based on theoretical insights in order to provide a detailed strategic sourcing process, which adopts a risk‐based orientation. Each of the five main steps of the strategic sourcing process that are identified have aspects related to the supply chain resilience that need to be taken into consideration on the sourcing decision‐making. The framework that is presented provides relevant insights regarding on how to effectively conduct the strategic sourcing process with the prescription that decision‐making needs to be transparent. It is hoped that the constructs presented in this paper encourage researchers to conducting empirical studies in order to obtain evidence related to the strategic sourcing process and its relations with supply chain resilience. The proposed framework of this paper, can also potentially support practitioners involved in the procurement and strategic sourcing activities by providing an effective guidance as to how to appropriately engage in decision‐making with respect to strategic sourcing. The approach proposed is unique, bringing a timely and novel approach by relating strategic sourcing with supply chain resilience during disturbing events.

A Structured Perspective on Behavioral Accounting Research

The objective of this study is to analyze and examine the research area of behavioral accounting. As today’s society is increasingly focused on the general state of human well-being, it is reasonable to assume that this field of social sciences will continue to receive the significance gained through contributions to social life. From the perspective of illustrating the evolution of research conducted in the field of behavioral accounting and starting from the evidence that this field of accounting has developed from the empirical to the theoretical dimension, the criterion of temporal succession of manifestation is used. The descriptive analysis technique is used for data analysis. The research results emphasize the constant concern and permanent diversification towards the humanemotional component of accounting, through the systematic observation of people and the measurement of variables.

Graph representation learning in bioinformatics: trends, methods and applications.

Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.

Emergent Systemic Hierarchies and their Relevance to Project Governance

AbstractThe objective of this study is to create an awareness of the emergent systemic hierarchies in the project environment, as well as their relevance to the design of project governance systems. A method is developed to identify the systemic hierarchy associated with the system of interest. The associated hierarchies of enabling systems and the user system are extracted.The method used to extract the systemic hierarchy stems from systemic characteristics described in the Soft Systems Methodology. A simple metaphor is used to guide the thinking in the extension of these systemic hierarchies.These resultant hierarchies provide a structured perspective of the levels in each hierarchy, the links between hierarchies as well as the emergent properties at each level in the hierarchy. Utilising the metaphor, specific work associated with each level in a hierarchy may be identified, quantified, and allocated during the initial design phase of a project organisation itself.

Desire of Use: A Hierarchical Decomposition of Activities and its Application on Mobility of Blind and Low-Vision Individuals.

Blind and low-vision individuals often have severely reduced mobility, affecting their quality of life and associated socioeconomic cost. Despite numerous efforts and great technological progress, the only used primary mobility aids are still white canes and seeing-eye dogs. Furthermore, there is a permeating tendency in the field to ignore knowledge of both mobility and the target group, as well as constantly design new metrics and tests that makes comparisons between solutions markedly more difficult. The Desire of Use model is introduced in an effort to promote a more holistic approach; it should be generalizable for any activity by any user, but is here applied on mobility of blind and low-vision individuals by a proposal and integration of parameters. An embodiment of the model is presented and with it we show why popular mobility metrics of today are insufficient to guide design; what tasks and metrics that should provide better understanding; as well as which fundamental properties determine them and are critical to discuss. Desire of Use has been introduced as a tool and a theoretical framework, and a realization has been proposed. Desire of Use offers both a structured perspective of pertinent design challenges facing a given solution, as well as a platform from which to compare test results and properties of existing solutions; in for example the field of electronic travel aids it should prove valuable for designing and evaluating new tests and devices.

A structured perspective of volumes on active learning

We approximate the version space which covers all feasible classification hypotheses into a structured geometric hypersphere against agnostic distribution. We present a structured perspective that divides the available active learning (AL) sampling approaches into two kinds of strategies: Outer Volume Sampling and Inner Volume Sampling. For the outer volume, it is represented by a circumscribed hypersphere which would exclude any outlier (non-promising) hypothesis from the version space globally. While for the inner volume, it is represented by many inscribed hyperspheres, which cover most of hypotheses within the outer volume. To enhance the performance of AL, we aggregate the two kinds of volumes to eliminate their sampling biases via finding the optimal inscribed hyperspheres in the enclosing space of outer volume. We then propose a Volume-based Model for the AL sampling without any distribution assumption. To generalize our theoretical model, in a non-linear feature space, spanned by kernel, we use sequential optimization to globally optimize the original space to a sparse space by halving the size of the kernel space. Then, the expectation maximization (EM) model which returns the local center helps us to find a local representation. To describe this process, we propose an easy-to-implement algorithm called Volume-based AL (VAL). Empirical evaluation on a various set of structured clustering and unstructured handwritten digit data sets have demonstrated that, employing our proposed model can accelerate the decline of the prediction error rate with fewer sampling number compared with the other algorithms.

Capture and evaluation of innovative ideas in early stages of product development

PurposeThe purpose of this paper is to propose a structured procedure for the capture and evaluation of innovative ideas in the early stages of product development. The procedure is designed to take account of internal and external factors affecting the value of innovative ideas.Design/methodology/approachThe proposed procedure is based on divergent and convergent approaches to innovation. Ideas are generated and captured using targeted questions and subsequently evaluated using modifications of the Kano model and failure mode and effect analysis (FMEA). Idea evaluation considers four criteria – delight, originality, market share potential and simplicity – which are used to compute an “idea priority number”.FindingsA case study looking at innovations in swimming goggles verified the validity of the proposed procedure. The procedure also supports fact-based decision making and applies a structured perspective to the innovation process that makes it easier to manage.Research limitations/implicationsThe case study made use of a pilot survey in which 32 customers participated. Although the case study was only intended as a demonstration of the method’s use, such a sample size could lead to unreliable results in certain cases.Originality/valueApplications of standard or expanded FMEA do not add value to a product because the main purpose of the approach is to prevent failure. The proposed inversion of FMEA logic combined with customer view via the Kano model offers a method for the structured analysis of product innovations. No similar approach to the evaluation of innovations has so far appeared in the literature.