Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.
Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.
Competitiveness is a multi-dimensional concept that organizations must inevitably strengthen themselves in all its dimensions to develop competitive power. Competitiveness is effective on the success of an enterprise in a worldwide level. Accordingly, the purpose of this research was to design a communicational model between the competitiveness types of small and medium industries in Iran. Regarding the research purpose, the study is applied, which is conducted based on the descriptive-causal method. The statistical population of this research in identifying factors affecting competitiveness includes all small and medium enterprises in Qazvin province, Iran. In order to design a model for competitiveness, the experts familiar with the issue of competitiveness were used. Accordingly, two questionnaires were used in this research to collect data. This questionnaire is distributed among members of the statistical sample after the determination of validity and reliability. The exploratory factor analysis, the Interpretative-Structural Modeling (ISM), and Structural-Equation (Path) Model (SEM) have been used to analyze the data. The results findings indicated that among the factors influencing competitiveness, the competitiveness factors related to firm management and the competitiveness factors related to suppliers and resources have the most impact on the competitiveness of small and medium firms. The results (findings) also showed that the competitiveness factors related to demand and customers are recognized as an absolutely influential component. The results of the Structural-Equation (Path) Model (SEM) have evaluated the path coefficients significance. According to the research's findings, it can be said that the factors affecting the competitiveness related to firm management and competitive factors related to suppliers and resources should be firstly strengthened in order to make small and medium industries more competitiveness and stimulate resources.
Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework's applicability using an actual usage scenario and left the implementation of the system for future work.
Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development methods and types of DSLs vary for different applications and objectives. In this study, we conducted a systematic literature review to overview DSL methods and their intended applications for VA systems. Moreover, the review outlines the benefits and limitations of each of these methods. The aim is to provide decision support for both the research and development communities to choose the most compatible approach for their application. We think the communication of this research delivers a broad figure of previous relevant research and assists with the transfer and adaptation of the results to other domains.
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