Exploring smart phone improvements based on a hybrid MCDM model
Introduction
Smart phones are electronic devices that combine the universal mobile phone and the personal computer. They integrate useful functionalities such as e-mail, web browsing, audiovisual amusement, word processing, mobile video, and a Global Positioning System (GPS). With the technological advancements of semiconductors, wireless communication, multiprocessors, real-time operating systems and high performance memory capacity, the performance and the size of smart phones have continually improved. Moreover, smart phones enable mobile conveniences, such as electronic payments, broadband internet access, high computing and communication performance, and multimedia platforms. According to investigation of the Gartner Group (Gartner), smart phones were considered the most desired consumption device by American consumers in 2011, thus surpassing mobile phones, e-book readers, media tablets and gaming machines. Gartner also predicts that the smart phone market will continue to expand and rapidly create significant business opportunities. Smart phones exemplify the multitasking era, or the M-era, by possessing the multiple features previously mentioned. Smart phone usage has continued to grow via the advancement in mobile marketing (Watson, McCarthy, & Rowley, 2013). Academic research involving smart phones, however, is scarce. Hence, the issue of smart phone has become more significant.
The study uses a hybrid MCDM (Multiple Criteria Decision Making) model combining the DANP (DEMATEL-based ANP) with VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) to reduce the gaps corresponding to each dimension/criterion. This improvement model can provide useful information to enterprises regarding how to optimally satisfy customer needs. By adopting the realized improvement scheme, enterprises consequently attain a high enterprise competitive advantage. In this study, challenges to the interrelationship and influential weights among the dimensions/criteria of smart phones are resolved by MCDM, which Kleijnen (2005) explained as a method that can consider the methodology of multiple decision-attributes simultaneously with priority ranking and evaluation according to the feature/attribute of each alternative, thus helping decision-makers when faced with limited feasible alternatives. This study also uses DEMATEL (Decision Making Trial and Evaluation Laboratory), a technique developed by the Geneva Battelle Research Center in 1972 that is effective for understanding the causality structure by observing the degree of factor interactions (Tzeng et al., 2007, Tzeng et al., 2007). Moreover, this study adopts the ANP (Analytic Network Process), which Saaty (1996) noted can address problems of interdependence and feedback among criteria. Hence, we use the inter-relational influence matrix of the DEMATEL technique with the basic concepts of the ANP to determine the influential weights of dimensions/criteria (called DANP). By using the DANP, individual weights and rankings can be used to determine the key factors for decision-making. Finally, the optimal decision-making alternative can be obtained with VIKOR, which probes enterprises’ problems to suggest how to lessen the gaps in dimensions/criteria for achieving a specific aspiration level.
The traditional model (Büyüközkan, 2009, Büyüközkan et al., 2008, Işıklar and Büyüközkan, 2007, Chen et al., 2010, Zavadskas and Turskis, 2011) assumes that the criteria are structured independently and hierarchically; however, in real world problems, the relationships between the criteria or dimensions are often interdependent and sometimes provide feedback-like effects. Büyüközkan et al. (2008) and Büyüközkan (2009) listed many MCDM methods, but assumed independent criteria in a hierarchical structure (such as additive model and weighting by AHP). In addition, the traditional model can only conduct the selection/ranking (achieving the relatively optimal result) without improvement for achieving the aspiration level. So this study attempts to achieve the aspiration level using an empirical case of an improvement plan for the smart phone and seeks to contribute to grasp the improvement strategies for promoting competitiveness of smart phones with a hybrid MCDM model based on an influential network relation map (INRM) and the degree of influence derived from the DEMATEL technique and the gaps derived from VIKOR method. Fig. 1 illustrates the basic concepts of problem-solving (Liou and Tzeng, 2012, Tzeng and Huang, 2011). Responses and social and personal attributes of the objectives may be represented as a data set (e.g., crisp, fuzzy set, and rough set) via investigation of the objectives. The data could be analyzed by using MCDM. MCDM could help decision-makers when faced with multiple-objective or multiple-attribute problems (Tzeng et al., 2007, Tzeng et al., 2007, Chiu et al., 2013, Lee et al., 2013, Liou, 2013, Liou and Tzeng, 2012).
The paper is organized in the following order: Section 2 describes the evaluation attributes of the smart phone; Section 3 illustrates the research methods; Section 4 provides an empirical case analysis for smart phones; and Section 5 presents the conclusion.
Section snippets
Evaluation attributions of the smart phone
Smart phones have become a system intimately associated with areas such as personal needs, commercial applications, and Internet access. Young people play games, listen to music, and watch YouTube videos with smart phones to stay abreast of the latest trends. Previously, a computer would have been required to perform these actions (Pooters, 2010). A smart phone combines a general mobile phone with a personal digital assistant (PDA), which includes more convenient and mobile commercial services
Research methods
This section will establish the structure of the improvement model for promoting the smart phone’s product value with a hybrid MCDM model, which combined the DANP and VIKOR based on the total influence matrix T from the DEMATEL technique. This study aims to determine the influence relationship among dimensions/criteria with the DEMATEL technique in the DANP and to measure influential weights for each factor using the DANP based on the basic concept of the ANP. The DANP can successfully
An empirical case analysis for improving the smart phone
This section explores the degrees of influence among the factors and determines the influential weights of each factor to find the key factor that each mobile communication enterprise should consider when popularizing smart phones. This process is performed using the influential weights of the DANP and VIKOR with a hybrid MCDM model. We consider five enterprises of smart phones for our case study to explain and to verify the key factors for smart phone development.
Conclusion
This study explores smart phone improvements by using a hybrid MCDM model, a method which determines an interrelationship matrix and builds an INRM for solving the relationship problems in the real world. The dimensions and criteria that influence the performance of smart phones have been identified in prior literature. These are outlined in this study and serve as bridging mechanisms for the smart phone enhancements. There are two main contributions of this study. First, the evaluation of
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2020, Journal of Cleaner ProductionCitation Excerpt :The main contribution of this paper is the development of an evaluation framework from multiple user perspectives and integration of grey theory-based Decision-making trial and evaluation laboratory (grey-DEMATEL) and Analytic Network Process (ANP), hereafter called as the gDANP method for computing inhibitors interrelationships and priority weights. The previous studies, researches have integrated two or three techniques (Büyüközkan and Güleryüz, 2016b; Hsu and Liou, 2013; Hu et al., 2014) (refer Table 1), although there is no study which combines grey system theory-based DEMATEL and ANP method for analysing mutual relationships of criteria and importance weight computation. Based on the result analysis, this research significantly contributes to policy implications for improving the share of intermodal freight transport in global freight distribution.