A Survey Based on Product Usability and Feature Fatigue Analysis Methods for Online Product

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1 A Survey Based on Product Usability and Feature Fatigue Analysis Methods for Online Product Nirali Patel, Student, CSE, PIET, Vadodara, India Dheeraj Kumar Singh, Assistant Professor, Department of IT, PIET, Vadodara, India Abstract The Rise of E-commerce sites has made web an excellent source for purchase product without wasting time. For taking purchase decision and product designing usability evaluation and feature fatigue analysis are useful. In this paper, methods of usability evaluation and feature fatigue analysis are described. Usability evaluation and feature fatigue analysis uses online reviews as data source and identify issues for use it and described the review mining process. Application of usability evaluatin, feature fatigue analysis and review mining are given. Keywords: Product usability, Feature fatigue analysis, Review mining, Online reviews Introduction People use multiple products to complete specific purpose at any time. For taking purchase decision of products, usability of that product is most useful. So product usability evaluation is indirectly useful for customer satisfaction. Customer satisfaction is most important for growth of any business. Customer prefer to choose that products which have maximum usability which provide customer satisfaction. For that customer prefer to that products which have more feature. But after use that products customer become dissatisfied by that products features. Feature fatigue analysis is use for identify that feature. Online reviews contain updated customer opinions. These reviews are uses as data source. For retrieving these reviews from web, review mining is use. Usability evaluation and feature fatigue analysis are use for solve the product related problems. For usability evaluation and feature fatigue analysis online reviews are uses as data source. For retrieving these reviews, review mining process is used. Product Usability Usability evaluation is now considered an important procedure in consumer product development. However, planning and conducting usability evaluation requires considerations of a number of factors surrounding the evaluation process including the product, user, activity, and environmental characteristics[2]. A product usability evaluation is an evaluation planned to validate the product-user interaction. Product usability evaluation is known as usability testing. Product usability evaluation is help us to recognize the quality of design, idea or concept according to usage. International Standards Organization in the ISO (1998) Guidance of Usability is defined as the effectiveness, efficiency and satisfaction with which specific users achieve specific goals in a specific context of use[1]. Main goal of usability is to have products established to maximize the users ease of use. Feature Fatigue The term feature fatigue (FF) was first used by D. V. Thompson, R. W. Hamilton, R.T. Rust (in 2005) to represent the phenomenon of customers inconsistent satisfaction with high-feature products before and after use. Customers desire to purchase products with additional features before use, but after using the products, they become overwhelmed by the complexity of these high-capability products and annoyed by the features they realize they don t want or need. They may become dissatisfied or frustrated with the usability problems of the products. This phenomenon is called feature fatigue [3][4]. FF will lead to customer dissatisfaction and wide spread negative word-of-mouth (WOM). Feature fatigue will decrease customer satisfaction and negatively impact manufacture s long term profit so it is essential to remove feature fatigue for manufacturer. For example, the BMW 7-Series cars whose dashboard contains above 700 features[6]. This kind of high capacity car is truly attractive in the first moment, but most of the owners are frustrated by the multi-function displays and multi-step options in the complex system, and their dissatisfaction will affect BMW s sale in a long term. For the purpose of remove Feature Fatigue, Feature should be integrated to balance initial income and long term profit. Customer can also give their feedback or opinion online. Massive Online customer reviews on similar product and features are uses as a data source for usability evaluation and feature fatigue analysis. So web mining approach is used. Web crawler is use for review mining. Product reviews can reflect the most updated customer opinions on product usability. Open Access Journals Blue Ocean Research Journals 53

2 Review Mining Nowadays, more and more people purchase products online and post reviews on products on the Web. Compared to survey data, online reviews contain the most updated customer opinions that reflect the evaluation information of product usability from customers' perspective. Online reviews are one form of WOM. Massive Online customer reviews on similar product and features are uses as a data source for usability evaluation and feature fatigue analysis. Process of retrieving customer reviews from webpages is known as Review mining. Web crawler is use for retrieve product reviews from webpages. Methods Of Usability Evaluation Traditional Methods Traditional methods include Cognitive modeling methods, Inspection methods, Inquiry methods, Prototyping Methods. Cognitive modeling involves creating a computational model to estimate how long it takes people to perform a given task. Inspection method involves observation of users by an experimenter, or the testing and evaluation of a program by an expert reviewer. Inquiry method involves collecting qualitative data from users. Inquiry method is also known as survey. In prototyping method, designer creates a prototype of the system. Instead of creating the complete final system, the designer may test different sections of the system. Traditional methods of usability evaluation are usually carried out using prototypes which are not available until in the later stage of product development. Method based on association rule mining [8] Web mining is used to examine product usability. It firstly uses a web crawler to collect online reviews. Then, it utilizes association rule mining techniques to extract customer opinions on the usability of product features. Then the Apriori algorithm is adopted for mining association rules, and a classifier is constructed to identify whether a review sentence is related to a feature s usability. Then product features usability are evaluated using the rules. Where, U = αnn - NP NN = number of negative review sentences NP = number of positive review sentences, α = importance level of negative attitude relative to positive one, which is determined by practitioners. The higher the score of U, poorer the usability. For example, for Logitech Harmony 890 s feature like setup there are 120 negative review sentences and 84 positive review sentences[8]. For find usability of that smart phone, α is take 2. NN= 120, NP= 84, α= 2 U= αnn NP = 2(120) 84 =156 U is higher means usability of that Logitech Harmony 890 s setup is poor. TABLE I. Pros and Cons of Usability Evaluation s Methods Traditional Methods Method based on Association rule mining [8] Performed in the product definition stage, can save money. a) using prototypes which are not available until in the later stage of product development b) Take more time Not efficient for limited reviews Open Access Journals Blue Ocean Research Journals 54

3 Methods Of Feature Fatigue Analysis Behavioral Decision Making Theory [5] This method based on behavioral decision making theory. For this they adopt six dimensional perceived value (PV) model. It use for analyze the effect of adding features on customer s perceived value before use and after use. They also propose analysis model to analyze feature fatigue quantitatively. It is use for customer to decision of repurchase based on PV value. Where, of the CPV = (CPB CPR) CPV= overall customer s PV of the product; CPB= overall customer s perceived benefits product; CPR= overall customer s perceived risk of the product; After working with feature overloaded products, customer achieve lower PV than expected before use means product not good as they predicted. They just study the effect of number of features on customer s PV, ignoring quality, appearance, price and other factors that may affect customer s PV. Six Dimensions are Financial, Functional, Physical, Psychological, Social and Time. According to quantitative FF analysis model FFI=. Manufacturers can make decisions based on a threshold (ϕ) of the FFI: while FFI ϕ, the feature should be integrated into the product. The threshold (ϕ) can be determined by practitioners based on expertise. The higher the FFI is, the lower doubtless the feature will make customers suffer from FF. Thus, the features with higher FFI will be incorporated into the product more preferentially than those with lower FFI. For example, before use customer s PV is 50 and after use customer s PV is 25 and threshold (ϕ) is 1 for smart phone s battery. So FFI is, Bayesian network [6] Bayesian network method is used for the uncertain nature of customer preferences. To resolve these problem, a probability based methodology for feature fatigue analysis is proposed. Bayesian network techniques are used to represent the uncertain customer preferences for capacity and usability. First calculate probability of Capability and complexity for Bayesian network. Then construct Bayesian network for capacity and complexity. Then do the FF analysis. This method can guide decision-makers in marketing and engineering to make the most powerful decisions in the process of product development. SIR Epidemic Model And Genetic Algorithm [7] SIR epidemic model and genetic algorithm to help designer to find optimal feature combination that maximize CE. SIR epidemic model used for analyze customer purchase behaviour under different feature combination. Genetic algorithm is use for search an optimal feature combination. Customer transaction affected by many factor such as product capability, usability, advertisement, price, quality, and customer service. According SIR epidemic model, prospects refer as susceptible, customers refer as infected and defectors refer as recovered. If product usability high means positive word of mouth than prospector will turn in customer. If product capability also high than prospects will turn in customer. If usability is low means negative word of mouth then prospects will turn in defectors. Genetic algorithm is use for optimal feature combination. For this first define potential feature. Then use genetic algorithm to find the optimize features which combination maximize CE. Using Usability Evaluation [8] FF analysis is perform using features usability. Value of FFD is lower it is better. FFD = U C With, U =, FFI= = 25 / 50 = 0.5 FFI is lower than threshold (FFI > ϕ) for smart phone s battery. So that smart phone s battery is suffer from FF. C = Where, U and C are normalize scores of usability and capability of future, FU is the usability score of the feature, FC is the capability score. Open Access Journals Blue Ocean Research Journals 55

4 Value of FFD is lower it is better. A feature is clear as a FF feature while its FFD value is larger than zero (FFD > 0). For example, for Logitech Harmony 890 s Feature fatigue analysis is given in table II. TABLE II. Case Study[8] Feature LCD, AB(Active Button) is good because value of FFD is low. TABLE III. Pros and Cons of Feature Fatigue Analaysis Methods Behavioral decision making theory[5] Effective for obtaining the FFI For Repurchase decision making Theoretical approach, experiment study not done Use only six dimensions, there may be other dimensions that impact customer Bayesian network [6] SIR epidemic model and genetic algorithm [7] Efficiency handle uncertain behavior of customer Understand customer behavior Fewer parameter need to be estimated because probability distribution depend on node s patents Can handle bidirection interference problem Optimal feature combination Understand customer transaction behavior Maximize customer equity(ce) PV Add/Delete feature than rebuild Bayesian network from initial step data gathering Focus only present customer preference Profit of selling product ignore cross selling Ignore ripple effect of WOM Using usability evaluation [8] Provide design support to designer Provide decision support for customer Not efficient for limited reviews Not use for new product Computationally expensive Issues In Usability Evaluation And Feature Fatigue Analysis Usability evaluation and Feature fatigue analysis uses online customer reviews as data source. There are main two issues for use online customer reviews: i) Fake Reviews or Review Spam and ii) limited number of reviews. A) Fake reviews are effect usability and feature fatigue analysis. It also effect on customer decision. Customer take wrong decision because of fake reviews. Fake reviews are classified in three categories: Type 1 (untruthful opinions): Those that purposely mislead readers or opinion mining systems by giving undeserving positive reviews to some target objects in order to promote the objects (which we call hyper spam) and/or by giving unfair or malicious negative reviews to some other objects in order to damage their reputation (which we call defaming spam). Type 2 (reviews on brands only): Those that do not comment on the products specifically but only the brands, the manufacturers or the sellers of the products. Although they may be useful, we consider them as spam because they are not targeted at the specific products and are frequently unfair. Type 3 (non-reviews): Those that are non-reviews, which have two main sub-types: (1) advertisements and (2) other irrelevant reviews containing no opinions (e.g., questions, answers, and random texts). B) For some unpopular products or new products number of online reviews are usually limited. Using limited or small number of reviews usability evaluation and feature fatigue analysis is difficult. Review Mining On E-commerce site customer can also give their feedback or opinion. Online customer reviews on product and features are uses as a data source for usability evaluation and feature fatigue analysis. Web crawler is use for retrieve reviews from webpages. Figure 1 shows the process of web crawler. The web crawler start new search from the unsearched URL list, accesses that URL and downloads the equivalent webpages through HTTP. Open Access Journals Blue Ocean Research Journals 56

5 Then analyzes the webpage contents. Finally output is product reviews and store them into the database. source. Using online reviews might be issues like fake reviews or review spam, limited number of reviews and customer uncertain behavior can be a good research work. Usability evaluation using web mining plays a vital role in research area. In web mining reviews are retrieving using review mining. Review mining process is also given. We also show application of usability evaluation, feature fatigue analysis and review mining. It could be possible to develop a more accurate algorithm for usability evaluation and feature fatigue analysis. References [1] ISO Ergonomic requirements for office work with visual display terminals (VDTs) Part 11: Guidance on usability (1998). [2] Kwahk, J., & Han, S. H.. A methodology for evaluating the usability of audiovisual consumer electronic products. Applied Ergonomics(2002), 33(5), [3] Thompson, D. V., Hamilton, R. W., & Rust, R. T.. Feature fatigue: When product capabilities become too much of a good thing. Journal of Marketing Research (2005), [4] Rust, R. T., Thompson, D. V., & Hamilton, R. W.. Defeating feature fatigue. Harvard Business Review(2006), 84(2), Fig. 1. Process of review mining. [8] Applications In our daily life every one use multiple products. At purchase time customer confused, which product they buy. To take purchase decision to customer usability evaluation and feature fatigue analysis are useful. To develop any product designing is main problem. Which features combinations is good for increase product selling is useful for successful designing and developed product. To take product designing decision usability evaluation and Feature fatigue analysis are useful for developer. It is useful to developer to develope new product with optimal feature. To solve product related problems usability evaluation and feature fatigue analysis is used, and it uses online review as data source. To retrieve online reviews, review mining is used. Conclusion And Future Work In this paper, we give the introduction of product usability, feature fatigue analysis and review mining. We also surveyed different methods for usability evaluation and identify or alleviate feature fatigue. All of methods are powerful and efficient in nature, but it has also limitation. Usability evaluation and feature fatigue analysis are used online reviews or word of mouth (WOM) as data [5] Mingxing Wu; Liya Wang. Feature Fatigue Analysis Based on Behavioral Decision Making. Industrial Engineering and Engineering Management (IEEM) (2011). [6] Li, M., & Wang, L.. Feature fatigue analysis in product development using Bayesian networks. Expert Systems with Applications (2010), [7] Wu, M., Wang, L., Li, M., & Long, H.. An approach based on the SIR epidemic model and a genetic algorithm for optimizing product feature combinations in feature fatigue analysis. Journal of Intelligent Manufacturing (2013), [8] Wu M., Wang L., Li M., & Long H.. An approach of product usability evaluation based on Web mining in feature fatigue analysis. Computers & Industrial Engineering 75 (2014), Open Access Journals Blue Ocean Research Journals 57