The Study on the Factors Influencing the Updating Decision Making of App Store Consumer.doc

The Study on the Factors Influencing the Updating Decision Making of App Store ConsumerHe Changzheng and Zhang Tingting(School of Business, Sichuan University, Chengdu 610064)Abstract: Facing the rapid development of Internet and mobile app stores, mobile apps companies have adopted a positive R&D strategy, the application of product life cycles gradually shortened. However, in the face of such rapid product updates, consumers did not show a positive response, what factors affect the consumers replacement decision, has become the important issues of application developers and corporate concerns. Application store consumers, combined with data mining and empirical research methods, starting from the application of product characteristics and consumer behavior to explore the factors affecting the decision-making of consumers update, build consumer to update the decision-making behavior of decision-making factor model and consumer updates forecast model. Empirical research found that the timing of application updates, customer perceived value and shift costs to consumers update decisions have a significant impact. Finally, predicted results show that consumers update the decision-making behavior prediction model based on the influencing factors to build the update behavior of consumers with strong discrimination ability.Keywords: App Store; consumers updating decision-making; influencing factors; Logistic regression; Naive Bayes0 IntroductionWith the development of mobile Internet and mobile phones and other mobile intelligent terminal, the phone is from the traditional communication tools to the evolution of mobile Internet applications and services hosted terminal. The carrying of the mobile application store as the mobile Internet applications and services and trading platforms in the future development to the fore. U.S. Apple to launch its own application store in 2008, the first to create a new business model, and his great success in attracting major giants have entered to prove that the strategic position of the mobile application store in the future mobile InternetReferencesLei Zhenzhou. mobile application store story J. World Telecommunication .2009,10:15-18.Accelerate the development of mobile Internet and mobile applications for the rapid development of the application store, a number of Internet companies and software developers have launched their own client applications, and an active R & D strategy to make the application of product development cycles continue to shorten product updates more quickly. However, in the face of such rapid product updates, consumers did not show a positive response, even if this behavior is completely free. Why, then, consumers do not like the application of product updates? This problem is still plagued by application developers and operators have.App Store as a new form of belonging to a new thing, its launch in 2008, only a few years from now, the research literature for this problem is less. The research focuses on the application store app store profit model, developers and consumers three. For example, Bergvall-Kareborn and HowcroftBirgitta BK, Debra Howcroft. Mobile Applications Development on Apple and Google PlatformsJ. Communications of the Association for Information Systems. 2011,29(12):565-580. Birgitta BK, Michael Bjorn, Didier Chinchilla. Motivational profiles of toolkit users iPhone and Android developersJ. International Journal of Technology Marketing.2011, 6(1):36-56.studied the development of the application store, GhezziGhezzi A, Balocco R, Rangone A. How a new distribution paradigm changes the core resources, competences and capabilities endowment: the case of Mobile Application StoresJ. Mobile Business and 2010 Ninth Global Mobility Roundtable.2010.6:33-42.analyzed the application store model for the industry core competitiveness, HolzerA.Holzer;J.Ondrus. Trends in Mobile Application DevelopmentJ. Social Informatics and Telecommunications Engineering,2009,12(1):55-64.and OndrusA.Holzer;J.Ondrus.Mobile application market: A developers perspectiveJ. Telematics and Informatics.2011.28(1):22-31.from the perspective of developers to explore the mobile application development market trends, and trends of the summary made eight specific recommendations for developers in the application store customer behavior analysis, AyalewHY Ho, LY Syu. Uses and gratifications of mobile application users: Electronics and Information Engineering (ICEIE), 2010 International Conference , Aug 1-3,2010C. Kyoto : IEEE Xplore,c2011.in their paper on consumer behavior in the Apple App Store from qualitative and quantitative way the two angles were studies. The results showed that low-income group of young people with higher income groups more frequently to purchase the application. Although the results show that consumer behavior and the use of access devices, visualization applications, a priori knowledge of consumers about the application, ratings, screenshots and customer reviews are consumers when accessing the application considered factors.Proper and correct understanding of the consumers replacement decision is very important for enterprises, it can help marketers to better target potential users and to enhance product sales. Many scholars began to study the replacement decision of the consumers, for example, Dong Jin et alDong Jin, Wu Bo, Wu Ruijuan. Chinese consumer purchase intent model - amendment to the Fishbein rational behavior model J. Management of the world, 2009,14 (1): 121-129.explored the perceived value, customer satisfaction, switching costs and consumption time interval of four factors on consumer repeat purchase intention impact, DongjinHK Choi, D Kim, T Uhm, E Limpens. A sequence-based genetic map of Medic ago truncatula and comparison of marker nonlinearity with M. sativaJ. Genetics, 2004,166(3): 1463-1502Chinese consumers to explore the factors that affect consumer repeat purchase. The replacement of high-tech products and consumer update decision scholars are more concerned about the problem. In the consumers update decision influencing factors on the high-tech products, Guoli Fu and Mao ChuanhuThe Guoli Fu, Mao Chuanhu high-tech product replacement model J. Quantitative & Technical Economics Research .1999,16 (2).established a is product homogeneity alternative areas of high-tech product replacement model. Kim and SrinivasanTPN Bui, HB Kim, S Srinivasan ;Micro bacterium ginsengiterrae sp. Nov., a -glycosidase-producing bacterium isolated from soil of a ginseng fieldJ.International Journal of Systematic and Evolutionary Microbiology.2010,60(12): 2808-2812PDA update point were studied and found that the update costs and the increasing expectations of the product speed is the main factor affecting the consumer update. Chen and ZhouXH Huang, X Chen, L Cao, L Chen ;Involvement of PI3K/PTEN/AKT/mTOR pathway in invasion and metastasis in hepatocellular carcinoma: Association with MMP9J. Herpetology Research,2009,39(2):177-186. hat the impact of trust, satisfaction and the Obstacles of these three factors significantly affect consumers repeat purchase, which, service quality and perceived value affect the trust and satisfaction, switching costs decided to transfer obstacles.Through the above analysis can be found, most of these studies concern durable consumer goods or high-tech products, for consumer applications in the App Store update decision-making research have not yet been concern also for management recommendations and insights. This article from the consumer update upgrade behavior departure expectations will consumers to update the decision-making areas of research extended to the field of application store, and explore the characteristics of the application store consumers update upgrade behavior factors and the associated product characteristics and consumer behavior recommendations and basis for application developers and corporate new product planning, enhance application updates, and effective update of the forecast and identify consumer behavior and update the reasons, in order to develop effective and timely incentives to enhance consumer update initiative, to optimize the proliferation and development of the product.1 The Theoretical Basis and AssumptionsAt home and abroad due to the problem of this study is still in its infancy, has not yet formed a mature theory, the article uses the analogy method, respectively, from the more mature there are two similar acts, that is, high-tech products updates two dimensions and consumers to repeat purchase decision, combined with the characteristics of applications, the basic assumption of this article.1.1 Timing of Product Updates and Consumers Update DecisionSome scholars that the timing of new products on the market affect consumers perception of product value, the timing of the introduction of different products may differ, resulting in consumer purchase options there are differences. Update policies for perishable high-tech products, many scholars will be a new product time to market as a direct relationship to the factors of the enterprise product sales profits Eisenhardt, and Brown (1998), Druehl and Schmidt (2009 ) and other scholars have been the product of each update interval. From a consumer perspective, Lu Ruoyu and FENG Xu (2008) study suggests that consumers confirm whether the need for product replacement will be worried about too quickly perishable high-tech product updates, product depreciation fast, and thus choose holding money to be purchasedFENG Xu, Luruo Yu, Liu Dewen. Perishable nature of high-tech products during the update process J. Research management, 2008,29 (1) :59-61. Research has also said that upgrading too often produce the so-called pan-substitution disorder, this state of mind will enable consumers to reverse psychology, they will always worry about the products they buy will soon become obsolete, always waiting for the soon an update products appear under the enterprise product sales, and at the same time, the rules of replacement will increase this effect. Based on previous research, this article from the update rate of the three dimensions: the interval, period and stability assumptions:H1: apply the update, the opportunity to influence the consumers replacement decision.H1-1: the average update cycle positive impact on the consumers replacement decision.H1-2: The update interval for a positive impact on the consumers replacement decision.H1-3: The stability of the update cycle positive impact on the consumers replacement decision.1.2 The Customer Perceived Value with Consumers to Update the Decision-makingConsumer behavior data collected for further analysis found that the perception of different consumer evaluation of applications is different. Some scholars study also shows that the perceived value of consumer products will influence consumers purchasing decisions. Such as customer satisfaction, the definition of Day (1984) and Oliver (1991) pointed out that the so-called customer satisfaction is the consumer after the transaction for a particular transaction behavior, the overall evaluation. The evaluation of the consumer is a reflection of their satisfaction. In the marketing literature has always believed that customer satisfaction is the key to loyalty and repeat purchase. Banos and Asia (2002), Liu Xiang, user satisfaction is the customer repeat purchase intention variables, of the reasons the customer repeat purchase intention to make some degree of explanationJim G. Banos. Customer relationship management success of the mysteries: the perception of the customer M. Machinery Industry Press,2002:257 -260. The RI model Ming-Liang Chen (2003) also pointed out that the customer perceived value and customer satisfaction, positive impact on consumer repeat purchase intention. Therefore infer that the following assumptions:H2: consumers of previous generations and satisfaction, positive impact on consumers replacement decision.The consumption trend is an unconventional consumer behavior in the social life in the current pop phenomenon, mainly as a particular consumer or consumer advocate and the pursuit. Every update of perishable high-tech products not only from the performance of the products has improved, but also into the fashion elements, making it more and more the trend. The trend of things always will be extensive publicity and dissemination of organizational behavior that people have a herd mentality, it can be inferred, the more the trend of things, the more likely a cause for concern. Therefore infer that the following assumptions:H3: application of the trend of positive effects on consumers replacement decision.1.3 Transfer Costs and Consumer Replacement DecisionIn the upgrading of perishable high-tech products, many scholars have purchased from a consumer point of view of the cost of the transfer of new products, the transfer cost is divided into two types of order to the transaction costs and learning costs, and they update decisions for consumers were studied. Among them, Lu Ruoyu and FENG Xu (2008) study found that, in the face of rapid and frequent updates of the high-tech products, consumers in considering whether to buy perishable high-tech products often have concerns that consumers may be out products to transfer cost considerations and do not want to replace 13. Perishable high-tech products with superior performance, but have purchased the previous generation of products consumers buy new products there are switching costs, so is bound to bring new products to bring utility and conversion costs compared . Talking about here is the conversion costs include the purchase costs as well as learning to use new products to spend time cost. If you can not compensate for switching costs, consumers will choose to continue using the original product. For applications, free features to consumers without the expense of purchasing costs in the update, 3G traffic download the application, the costs of the traffic generated will bring consumers the cost of this fee depends on the application size. Therefore infer that the following assumptions:H 4: transaction costs, reverse affect the consumers replacement decision.Learn the cost of consumer expectations updated, need to learn and adapt to new features, new technologies, the time and costs. Update of the application of the product, although consumers before and after the update, use the same product, but due to the new version by adding more new features, allowing consumers to have to treat the new product to re-adapt to the new changes . In this paper that the new products, updates will affect the time to learn the new features, and consumers is accumulated not update the version number will increase the number of new content. Therefore, we assume:H5: to learn the cost of reverse affect the consumers replacement decision.H5-1: updates the total number of projects to reverse the update decisions that affect consumers.H5-2: the consumer of the product accumulated does not update the version number to reverse the update decisions that affect consumers.2 Consumers to Update the Decision-influencing Factors ModelBased on the above theoretical assumptions, the establishment of the consumer to update the decision-making influence factors Logit model (see formula (1). The mean of the timing of product updates - the version of the update cycle, the update interval, update cycle stability, customer perceived value - Consumer satisfaction with the previous generation product, the application of the trend, as well as transfer costs - transaction costs, updates the total number of projects, and consumers of the product is not cumulative to update the version number as independent variables, regression to the consumers replacement decision. Among them, the consumers have downloaded an application of historical versions of study, whether to download a new version of the application behavior as consumers update decision metrics. Updated all versions of a particular application cycle standard deviation as a measure of the update cycle stability; share similar products downloads downloads of a particular application as an application of the trend of the measure; the size of the application as a measure of transaction costs indicators. Previous generation products for consumer satisfaction, consumers have downloaded the product of historical versions of score as a metric, instead of scoring values obtained by questionnaire, main considerations are: consumers rate is the most primitive data, satisfaction data than the survey questionnaire to obtain more accurate and objective. Symbols and explanations of each variable in Table 1.logit Pi=ln(Pi1-Pi)=+1(TIME_M)i+2(TIME_R)i+3(TIME_S)i+4(CONS_G)ik+5(APP_D)i+6(APP_S)i+7(UPDATA_N)i+ 8(CONS_V)ik+ik (1)Table 1 On behalf of the symbols and explain each variable in the modelVariableExplanationVariableExplanationPikUpdate decision-making of the applicationsiconsumerk(update=1;do not update=2)(APP_D)iShare of the applicationsidownloaded in the downloads of similar products(TIME_M)iApplicationsiversion of the update cycle mean(APP_S)iThe size of the applicationsi(TIME_R)iIThe update interval of applications(UPDATA_N)iApplicationsiupdate the content of the total number of projects(TIME_S)iStandard deviation of applicationsiversion of the update cycle(CONS_V)ikApplicationsiconsumerkof the product accumulated does not update the version number(CONS_G)ikApplicationsiconsumerkhas to download the historical version of the product ratings3Analysis of Research Data and Model ResultsAll experiments were performed in memory of