Expert Systems with Applications 37(2010)6874-6884 Contents lists available at Science Direct Expert Systems with Applications ELSEVIER journalhomepagewww.elsevier.com/locate/eswa A framework of online shopping support for information recommendations Wen-Shan Lin,, Nathalie Cassaigne Tzung-Cheng Huan Information Systems, National Chiayi University, No 580, Sinmin Road, Chiayi 600, Taiwan, ROC b School of Informatics. The University of Manchester, P.O. Box 88, Manchester, M60 1QD, UK Graduate School of Tourism and Leisure, National Chiayi University, No 580, Sinmin Road, Chiayi 600, Taiwan, ROC ARTICLE INFO A BSTRACT Keywords The growth of e-commerce has caused problems with personalized recommendations. Although several Electronic commerce attempts have made to improve or automate the retrieval and filtering of such information, no gener E-marketing strategy oping support(Iss) framework links the semantic context of online shopping with shoppers purchases in order to improve the efficiency of online shopping support. Through the application of knowledge-modeling, this paper selects a college population to empirically investigate and establish the relationship between e- marke ing terms and shoppers'buying behavior. General online shopping and the online book purchases are selected to validate the generic framework. Two hypotheses are tested: (1)e-marketing terms are impo tant in influencing shoppers'decisions: and (2) shoppers behave differen espect to different types of buys. Experimental results indicate that shoppers perceive the impo of e-marketing terms differently whilst shopping online. Six types of shoppersare classified: (1) general-purpose, (2)security. concerned, (3)value. (4)fashionable, (5) time-sensitive, and(6)service-oriented. Results and future research opportunities are discussed. This paper serves as a basis for improving online information search shopping pu e 2010 Elsevier Ltd. All rights reserved. 1 Introduction searching information for supporting shoppers. Therefore, this pa- per applies the knowledge-modeling approach to model shoppers While the World Wide Web continues to gain in popularity, shopping behavior. We claim that the e-marketing strategies ap- accessing relevant information online is becoming difficult. Web plied by vendors to sell or promote their products online influence contents are characterized by large numbers of diverse information shoppers shopping-oriented searches. sources. In e-commerce, shoppers seek buying recommendatio This paper improves the knowledge of online shopping supports through search tasks of intelligent shopping supports(ISS), or by adopting a user-centric approach. The research objectives are: agents. It therefore becomes necessary for ISS to understand what (1)to identify and present the most influential e-marketing terms shoppers want and, at the same time, what information exists on in shoppers' decision-making: (2)to model online shoppers'deci- line. This poses challenges to ISS: not only are human minds diffi- sion-making styles; (3) to experimentally validate the research cult to understand, but the information available online is also framework among college students. ubiquitous and heterogeneous(Anupam, Hull, & Kumar, 2001 The framework was experimentally validated for cases of gen- shoppers face two main limitations. The first is that they do not its generosity for online shopping. Book purchases are selected be ave enough knowledge about the veracity of their search results cause this is a relatively mature online market(Labitzke, 1999)cat- ue to a lack of awareness about the information that commercial egorized Brenner, Zarneknowb, wittig, 1998 )as a tangible and web sites are required to provide the second is that shopping sup- homogeneous type of product. by buying this kind of tangible ports rely on firm attributes (e.g. price comparison) which may not product online, textual contents of online bookshops gain impor represent all factors taken into shoppers'considerations As a re- tance in affecting shoppers' decision-making. The paper concludes sult, shoppers do not have enough support when making their buy- with discussions and suggestions for future work. knowledge of search while shopping online to assist IsS in proposed research hypotheses. Section 3 presents research dea ing decisions. In other words, there is a need to acquire shoppers the literature and justifying th and methodology. The statistical results are addressed and ex Corresponding author. Tel: +886 5 2732895: fax: +886 5 2840929. plained in Section 4. Finally, the discussions and conclusions are E-mail address: wslin@mailncyu. edu. tw (W.-S Lin). stated in Session 5 0957-4174 front matter o 2010 Elsevier Ltd. All rights reserved. oi:10.1016eswa201003034
A framework of online shopping support for information recommendations Wen-Shan Lin a,*, Nathalie Cassaigne b , Tzung-Cheng Huan c aDepartment of Management Information Systems, National Chiayi University, No. 580, Sinmin Road, Chiayi 600, Taiwan, ROC b School of Informatics, The University of Manchester, P.O. Box 88, Manchester, M60 1QD, UK cGraduate School of Tourism and Leisure, National Chiayi University, No. 580, Sinmin Road, Chiayi 600, Taiwan, ROC article info Keywords: Electronic commerce E-marketing strategy Intelligent shopping support (ISS) abstract The growth of e-commerce has caused problems with personalized recommendations. Although several attempts have made to improve or automate the retrieval and filtering of such information, no generic framework links the semantic context of online shopping with shoppers’ purchases in order to improve the efficiency of online shopping support. Through the application of knowledge-modeling, this paper selects a college population to empirically investigate and establish the relationship between e-marketing terms and shoppers’ buying behavior. General online shopping and the online book purchases are selected to validate the generic framework. Two hypotheses are tested: (1) e-marketing terms are important in influencing shoppers’ decisions; and (2) shoppers behave differently with respect to different types of buys. Experimental results indicate that shoppers perceive the importance of e-marketing terms differently whilst shopping online. Six types of shoppers’ are classified: (1) general-purpose, (2) securityconcerned, (3) value, (4) fashionable, (5) time-sensitive, and (6) service-oriented. Results and future research opportunities are discussed. This paper serves as a basis for improving online information search for shopping purposes. 2010 Elsevier Ltd. All rights reserved. 1. Introduction While the World Wide Web continues to gain in popularity, accessing relevant information online is becoming difficult. Web contents are characterized by large numbers of diverse information sources. In e-commerce, shoppers seek buying recommendations through search tasks of intelligent shopping supports (ISS), or agents. It therefore becomes necessary for ISS to understand what shoppers want and, at the same time, what information exists online. This poses challenges to ISS; not only are human minds diffi- cult to understand, but the information available online is also ubiquitous and heterogeneous (Anupam, Hull, & Kumar, 2001; Domingue, Martins, Tan, Stutt, & Pertusson, 2002). Nevertheless, shoppers face two main limitations. The first is that they do not have enough knowledge about the veracity of their search results due to a lack of awareness about the information that commercial web sites are required to provide; the second is that shopping supports rely on firm attributes (e.g. price comparison) which may not represent all factors taken into shoppers’ considerations. As a result, shoppers do not have enough support when making their buying decisions. In other words, there is a need to acquire shoppers’ knowledge of search while shopping online to assist ISS in searching information for supporting shoppers. Therefore, this paper applies the knowledge-modeling approach to model shoppers’ shopping behavior. We claim that the e-marketing strategies applied by vendors to sell or promote their products online influence shoppers’ shopping-oriented searches. This paper improves the knowledge of online shopping supports by adopting a user-centric approach. The research objectives are: (1) to identify and present the most influential e-marketing terms in shoppers’ decision-making; (2) to model online shoppers’ decision-making styles; (3) to experimentally validate the research framework among college students. The framework was experimentally validated for cases of general online shopping and online book buy, thereby demonstrating its generosity for online shopping. Book purchases are selected because this is a relatively mature online market (Labitzke, 1999) categorized (Brenner, Zarneknowb, & Wittig, 1998) as a tangible and homogeneous type of product. By buying this kind of tangible product online, textual contents of online bookshops gain importance in affecting shoppers’ decision-making. The paper concludes with discussions and suggestions for future work. Section 2 begins by reviewing the literature and justifying the proposed research hypotheses. Section 3 presents research design and methodology. The statistical results are addressed and explained in Section 4. Finally, the discussions and conclusions are stated in Session 5. 0957-4174/$ - see front matter 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.03.034 * Corresponding author. Tel.: +886 5 2732895; fax: +886 5 2840929. E-mail address: wslin@mail.ncyu.edu.tw (W.-S. Lin). Expert Systems with Applications 37 (2010) 6874–6884 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
W-S Lin et aL Expert Systems with Applications 37(2010)6874-6884 68 2. Related works and hypotheses and product, and does not take into account the integrated product and service information with respect to e-marketing strategies ap- 2.1. Online shopping support plied by online vendors to promote and sell their products. Shoppers have demanded more personalized information deliv- 2. 2. E-marketing strategies ery services, such as intelligent shopping agents, to obtain informa- tion about products (Ringland Duce, 1987). Moreover, the In order to model e-marketing strategies, the conventional mar- behavior of an intelligent agent should emulate human behavior, keting strategies(Benman Evans, 2004: Mariotti& Gobbi, 2001): such as reasoning and problem solving ackson, 1999). In our case, place, people, product, promotion, price and process are consid shopping agents require intelligence and knowledge to process and ered. However, not all of the conventional marketing strategies find online information to meet shoppers' needs and styles. West can be applied to online markets due to limitations and differences (1991)asserts that one of the search functions for dynamic Inter- addressed in the literature relative to electronic commerce( Chan net search is to match sellers'offers with buyers' preferences and Krant, 2000: Gallmanm, 1996: Labitzke, 1999). Several studies y Lau, Hofstede, and Bruza(2000) who states that intelligent claim that these six strategies are applicable with modifications hopping agents' memory should hold profiles of shoppers prod-(Chaffey, Mayer, Johnston, Ellis-Chadwick, 2003: Clow Baack uct requirements as well as knowledge of a particular product do- 2002). The framework of e-marketing strategies proposed in this main. Karacapilidis and Moraitis(2001)proposed a web-based paper (Table 1)indicates the relationship between this framework ommerce shopping support in which customers can delegate and conventional marketing strategies. Six categories of e-market tasks to their personal software agent. However, the shopping ng strategies are identified In this framework, the main amend- port only recognizes the criteria of performance(e.g. costs and ment is the replacement of "people"by computer-mediated safety issues). Similarly, the knowledge base of the shopping electronic commerce(CMEC); virtual shop assistants are provided. support, named Alice(Domingue et al, 2002)is limited to price The term CmEC was coined by researchers to denote online com The framework of e-marketing strategies. Strategies Price Conventional marketing Physical shop Product Price E-marketing strategy Place, the web stores Computer-mediated electronic Product Promotion Price Process, e.g. onal shopping cart Place. the web store CMEC Product Promotion nce Process (02)presented in preferred languag web store front by displaying ste map or (08)sale quick check-out product(s) discounts (014)order the ava ll ntry if it is stated products O9)Free shipping formation is giv 010)special items of privacy pol 011)new prod review about (o8.7)list price recommended shipping option support 019)FAQ sectio (o18)gt service(020)retum polio icture is displaye Fig. 1. A hierarchy of e-marketing terms
2. Related works and hypotheses 2.1. Online shopping support Shoppers have demanded more personalized information delivery services, such as intelligent shopping agents, to obtain information about products (Ringland & Duce, 1987). Moreover, the behavior of an intelligent agent should emulate human behavior, such as reasoning and problem solving (Jackson, 1999). In our case, shopping agents require intelligence and knowledge to process and find online information to meet shoppers’ needs and styles. West (1991) asserts that one of the search functions for dynamic Internet search is to match sellers’ offers with buyers’ preferences and by Lau, Hofstede, and Bruza (2000) who states that intelligent shopping agents’ memory should hold profiles of shoppers’ product requirements as well as knowledge of a particular product domain. Karacapilidis and Moraitis (2001) proposed a web-based ecommerce shopping support in which customers can delegate tasks to their personal software agent. However, the shopping support only recognizes the criteria of performance (e.g. costs and safety issues). Similarly, the knowledge base of the shopping support, named Alice (Domingue et al., 2002) is limited to price and product, and does not take into account the integrated product and service information with respect to e-marketing strategies applied by online vendors to promote and sell their products. 2.2. E-marketing strategies In order to model e-marketing strategies, the conventional marketing strategies (Benman & Evans, 2004; Mariotti & Sgobbi, 2001): place, people, product, promotion, price and process are considered. However, not all of the conventional marketing strategies can be applied to online markets due to limitations and differences addressed in the literature relative to electronic commerce (Chan & Krant, 2000; Gallmanm, 1996; Labitzke, 1999). Several studies claim that these six strategies are applicable with modifications (Chaffey, Mayer, Johnston, & Ellis-Chadwick, 2003; Clow & Baack, 2002). The framework of e-marketing strategies proposed in this paper (Table 1) indicates the relationship between this framework and conventional marketing strategies. Six categories of e-marketing strategies are identified. In this framework, the main amendment is the replacement of ‘‘people” by computer-mediated electronic commerce (CMEC); virtual shop assistants are provided. The term CMEC was coined by researchers to denote online comPlace, the web store CMEC Product Promotion Price Process (O1) offered product(s) (O2) presented in preferred language (O3) web site is located in a specific country if it is stated (O4) full contact information is given (O5) the availability of physical shop (O6) assortments of products (O7) the availability of privacy policy (O8) sale: today's special/ time-limited sale/ bundle sale/ online-only deals/ special offers/ discounts (O9) Free shipping (O10) special items (O11) new product (O12) recommended products (O13) online payment option / quick check-out (O14) order checking facility (O15) the friendliness of web storefront by displaying site map or navagational tool (O16) customer serivce/online support (O17) International shipping option (O18) gift service (O19) FAQ section (O20) return policy (O21) customer review about product (O22) product picture is displayed (O8.6) Sale price (O8.7) list price Fig. 1. A hierarchy of e-marketing terms. Table 1 The framework of e-marketing strategies. Framework Strategies Place People Product Promotion Price Process Conventional marketing strategy Physical shop Shopkeepers Product Promotion Price Process E-marketing strategy Place, the web stores Computer-mediated electronic commerce, e.g. navigational tools Product Promotion Price Process, e.g. shopping cart W.-S. Lin et al. / Expert Systems with Applications 37 (2010) 6874–6884 6875
W.-S. Lin et al/ Expert Systems with Applications 37(2010)6874-6884 Environment stimul (e. g, advertisement, interpersonal observation INPUTS Attentional and perceptual filter ation of wants C Memery(menton needs, necessary actions, outcomes perience, beliefs, Brand attitudes OUTPUTS Brand purchase intentions Social, economic, cultural, political impeding/ Fig. 2. Consumer information processing process [ Source: Baker, 1999) merce( Chaffey et al., 2003; Kiang, Raghu, Shang, 2000). This pa- available to shoppers. The process consists of the mental treatment per interprets CMEC as a vital differentiation between general and of this data as the consumer stores it, links it with existing ideas e-marketing strategies in the sense that the friendliness of web and memories, and evaluates its relevance to his or her goals tores(e.g. their navigational or search tools)is provided on a com The outputs are the consumers opinions about this data(e.g. an puter-mediated basis in electronic commerce ntention to buy or to postpone buying). The central information The term"customer service"has traditionally categorized brick process is most relevant to our study. In general, consumers partic and mortar shops as"process, "offering services at any point in the ipate in the information processing process. shopping process. However, it is included here under the categor A diagram is proposed to analyze buying behavior regarding dif- place, "since displaying service-relevant information helps shop- ferent types of products( Fig 3). Baker(1999)identifies four m pers understand the services offered by the web stores. Therefore, ket initiators in relation to customers'different buys customers can locate this information at their convenience before shopping. Another point is the category of"price. "No e-marketing(1)Complex consumer buy occurs when the consumer is highly term is directly linked to this category: however, it does affect volved and perceives the product as discontinuous. For shoppers buying behavior as relevant to their budget. The second this type of product, shoppers go through a cognitive learn- point needing clarification is Internet security. No direct e-market ing process (i.e. information search, brand evaluation, ing term is related to this concept. However, a level of web site detailed post-adoption appraisal). security is defined by the level of information provided about the (2)Dissonant buying occurs when the consumer is highly commercial side(e.g. full contact information, the availability of nvolved but sees no significant differences among the privacy policy, or the availability of a physical shop). Therefore rands, and buys the product in a hurry. Such consumers oth this"price"category and the concept of shop from a secur re likely to seek alternative brands that will meet their web site are included in the framework of shopping support Shop expectations pers'different buys are presented below. (3) Habitual buy occurs when there is no significant difference Based on the framework of e-marketing strategies, a hierarchy mong brands and the consumer is minimally involved in of e-marketing terms perceived by shoppers as important is cre- the purchase Extended information processing is unneces- ated based on the observations of well-known web sites(Fig. 1 ary, and experience is the safest guide Twenty-two e-marketing terms are considered important in influ (4) Variety-seeking buy occurs when there is little involvement encing a shopper's buying decisions and brand proliferation. The consumer chooses something To test the importance of e-marketing terms in affecting shop- new to relieve boredom pers'behavior in the decision-making process, Hl is proposed: Such behavior is typical of low-involved innovators. H1: E-marketing terms are important in influencing shoppers Nevertheless, shoppers in physical stores are categorized 2.3. Consumers' different types of buys ferent mixtures of product information are provided to meet online shoppers'needs. To take this notion into our study, different styles Consumer buying decisions are cognitive processes: an i of online shoppers search and evaluate information presented in tual sequence of thinking, evaluating and deciding Fig. 2 web shops by judging the perceived level of importance of e-mar rizes these processes(Baker, 1999). In Fig. 2, the inputs are keting terms. In order to classify types of online buys, the literature in traditional marketing is examined. Several types of shoppers are IListsofrecommendedwebsitesforbuyingproductsprovedbyhttplidentifiedanddocumentedasthemarketsegmentationstrategies (Ghosh, 1994: Liu Wei, 2002; Stephenson Willett, 1969: Swait
merce (Chaffey et al., 2003; Kiang, Raghu, & Shang, 2000). This paper interprets CMEC as a vital differentiation between general and e-marketing strategies in the sense that the friendliness of web stores (e.g. their navigational or search tools) is provided on a computer-mediated basis in electronic commerce. The term ‘‘customer service” has traditionally categorized brick and mortar shops as ‘‘process,” offering services at any point in the shopping process. However, it is included here under the category ‘‘place,” since displaying service-relevant information helps shoppers understand the services offered by the web stores. Therefore, customers can locate this information at their convenience before shopping. Another point is the category of ‘‘price.” No e-marketing term is directly linked to this category; however, it does affect shoppers’ buying behavior as relevant to their budget. The second point needing clarification is Internet security. No direct e-marketing term is related to this concept. However, a level of web site security is defined by the level of information provided about the commercial side (e.g. full contact information, the availability of privacy policy, or the availability of a physical shop). Therefore, both this ‘‘price” category and the concept of shop from a secure web site are included in the framework of shopping support. Shoppers’ different buys are presented below. Based on the framework of e-marketing strategies, a hierarchy of e-marketing terms perceived by shoppers as important is created based on the observations of well-known web sites1 (Fig. 1). Twenty-two e-marketing terms are considered important in influencing a shopper’s buying decisions. To test the importance of e-marketing terms in affecting shoppers’ behavior in the decision-making process, H1 is proposed: H1: E-marketing terms are important in influencing shoppers’ decisions. 2.3. Consumers’ different types of buys Consumer buying decisions are cognitive processes: an intellectual sequence of thinking, evaluating and deciding. Fig. 2 summarizes these processes (Baker, 1999). In Fig. 2, the inputs are the data available to shoppers. The process consists of the mental treatment of this data as the consumer stores it, links it with existing ideas and memories, and evaluates its relevance to his or her goals. The outputs are the consumer’s opinions about this data (e.g. an intention to buy or to postpone buying). The central information process is most relevant to our study. In general, consumers participate in the information processing process. A diagram is proposed to analyze buying behavior regarding different types of products (Fig. 3). Baker (1999) identifies four market initiators in relation to customers’ different buys: (1) Complex consumer buy occurs when the consumer is highly involved and perceives the product as discontinuous. For this type of product, shoppers go through a cognitive learning process (i.e. information search, brand evaluation, detailed post-adoption appraisal). (2) Dissonant buying occurs when the consumer is highly involved but sees no significant differences among the brands, and buys the product in a hurry. Such consumers are likely to seek alternative brands that will meet their expectations. (3) Habitual buy occurs when there is no significant difference among brands and the consumer is minimally involved in the purchase. Extended information processing is unnecessary, and experience is the safest guide. (4) Variety-seeking buy occurs when there is little involvement and brand proliferation. The consumer chooses something new to relieve boredom. Such behavior is typical of low-involved innovators. Nevertheless, shoppers in physical stores are categorized according to their styles. Vendors target styles of shoppers in order to meet shoppers’ needs and maximize profits. In other words, different mixtures of product information are provided to meet online shoppers’ needs. To take this notion into our study, different styles of online shoppers search and evaluate information presented in web shops by judging the perceived level of importance of e-marketing terms. In order to classify types of online buys, the literature in traditional marketing is examined. Several types of shoppers are identified and documented as the market segmentation strategies (Ghosh, 1994; Liu & Wei, 2002; Stephenson & Willett, 1969; Swait Social, business, cultural, political and economic environment Environment stimuli (e.g., advertisement, interpersonal observation) Attentional and perceptual filter Interpretation, formation and evaluation of wants, needs, necessary actions, outcomes Brand beliefs Brand attitudes Brand purchase intentions Social, economic, cultural, political impeding/ facilitating conditions Response (purchase/rejection) INPUTS CENTRAL PROCESSING OUTPUTS Short-term memory Long-term memory Experience, beliefs, attitudes goals, evaluative criteria, etc. Fig. 2. Consumer information processing process [Source: Baker, 1999]. 1 Lists of recommended web sites for buying products proved by http:// www.bizrate.com. 6876 W.-S. Lin et al. / Expert Systems with Applications 37 (2010) 6874–6884
ems with Appli 68 High Involvement Complex Buying Dissonant Buying Adaptors Innovators Habitual Buying Variety-Seeking ow Involvement Fig 3. Decision styles of market initiators(Baker, 1999). Shopper shopping based market segmentatio Market segmentation approach Author hopping oriented segments in physical shops Stephenson and willett(1969) he co he recreational shopper The price-bargain shopper The store-loyal shopper Fashion orientation segments in womens apparel market in physical shops Ghosh(1994) ality conscious Value oriented segments in physical shops Swait and Sweeney(2000) shion enthusiasts Timid and uninvolved Surfer and searcher segments in web stores This paper (1) Security-concerned shopper (4)Time-sensitive shopper (5)Service-oriented shopper able 3 Shoppers decision-making styles. Group Decision style ation needs Search criteria ites designed to diverse audiences with necessary pieces of Check information richness ation Searcher Security-concerned Responsive web sites Check the reliability of web site ast value Fashionable shopper information Time-sensitive shopper Useful web sites with high ordering efficiency and well organized contents ng time for first-time user should short Service-oriented Web sites with high helpfulness in terms of customer service and functional Check the level regarding to responsiveness of shopper features web sites Sweeney, 2000). Table 2 illustrates the literature and the pro- 2. 4. E-marketing semantic terms and their importance ecting posed online buys in our study Styles of online shoppers are seg- shoppers'behavior mented by the main factors that orient their buying processes fashion, value, and quality. The types of buys reflect the buying Different sets of e-marketing strategies are hypothesized as purposes. With references to the literature, this paper proposes having certain values in affecting shoppers' different types of buys two groups of online shoppers: surfers and searchers (Table 3). By relating the frameworks of e-marketing strategies to shoppers The surfer, a general-purpose shopper, prefers browsing online types of buys, a cross-framework is derived that details the e-mar information but generally does not intend to purchase. The search- keting terms for shoppers that engage in different types of buys ers study the specific information or conditions provided by web (see table 4). ites before making a purchasing decision. Five styles of shoppers in point, the general-purpose buy occurred when are identified: security-concerned, value, fashionable, time-sensi- shoppers look for web sites that offer online payment options tive shoppers, and service-oriented and shopping carts. Shoppers prefer to shop on web sites that of- In order to test and identify styles of shoppers, H2 is developed: fer customer service and full explanations. Table 5 depicts the contextual terms shoppers may look for while conductin H2: Shoppers behave differently with respect to different types ent types of buys. For the general-purpose buy, shoppers of products web sites that have full contact information customized
& Sweeney, 2000). Table 2 illustrates the literature and the proposed online buys in our study. Styles of online shoppers are segmented by the main factors that orient their buying processes: fashion, value, and quality. The types of buys reflect the buying purposes. With references to the literature, this paper proposes two groups of online shoppers: surfers and searchers (Table 3). The surfer, a general-purpose shopper, prefers browsing online information but generally does not intend to purchase. The searchers study the specific information or conditions provided by web sites before making a purchasing decision. Five styles of shoppers are identified: security-concerned, value, fashionable, time-sensitive shoppers, and service-oriented. In order to test and identify styles of shoppers, H2 is developed: H2: Shoppers behave differently with respect to different types of products 2.4. E-marketing semantic terms and their importance in affecting shoppers’ behavior Different sets of e-marketing strategies are hypothesized as having certain values in affecting shoppers’ different types of buys. By relating the frameworks of e-marketing strategies to shoppers’ types of buys, a cross-framework is derived that details the e-marketing terms for shoppers that engage in different types of buys (see Table 4). As a case in point, the general-purpose buy occurred when shoppers look for web sites that offer online payment options and shopping carts. Shoppers prefer to shop on web sites that offer customer service and full explanations. Table 5 depicts the contextual terms shoppers may look for while conducting different types of buys. For the general-purpose buy, shoppers look for web sites that have full contact information, customized options, Complex Buying Habitual Buying Dissonant Buying Variety-Seeking High Involvement Adaptors Innovators Low Involvement Fig. 3. Decision styles of market initiators (Baker, 1999). Table 2 Shopper decision-making styles for online shopping based market segmentation studies. Market segmentation approach Author Types of shoppers Shopping oriented segments in physical shops Stephenson and Willett (1969) The convenience shopper The recreational shopper The price-bargain shopper The store-loyal shopper Fashion orientation segments in women’s apparel market in physical shops Ghosh (1994) Price conscious Value conscious Quality conscious Value oriented segments in physical shops Swait and Sweeney (2000) Fashion enthusiasts Style seekers Classics Timid and uninvolved Surfer and searcher segments in web stores This paper Surfer: (1) General-purpose shopper Searcher: (1) Security-concerned shopper (2) Value shopper (3) Fashionable shopper (4) Time-sensitive shopper (5) Service-oriented shopper Table 3 Shoppers’ decision-making styles. Group Decision style Information needs Search criteria Surfer General-purpose shopper Web sites designed to diverse audiences with necessary pieces of information Check information richness Searcher Security-concerned shopper Responsive web sites Check the reliability of web sites Value shopper Best value Check value of produce Fashionable shopper Attractive and entertaining web sites Check the up-to-date information Time-sensitive shopper Useful web sites with high ordering efficiency and well organized contents Check the friendliness of information displayed The learning time for first-time user should be short Service-oriented shopper Web sites with high helpfulness in terms of customer service and functional features Check the level regarding to responsiveness of web sites W.-S. Lin et al. / Expert Systems with Applications 37 (2010) 6874–6884 6877
W.-S Lin et aL/ Expert Systems with Applications 37(2010)6874-6884 information about price, a product picture, promotion news and 3. Research method online payment options. While looking for this information, shop pers make a buying decision based on these e-marketing 3.1. Method terms General online shopping and book shopping are chosen for this study to test shoppers' search and decision-making behavior. 2.5. General online shopping and online book shopping Books are the most popular products over the Internet, as shown in GVUs WWw User Survey(Source: Georgia Tech Research Cor- Generalonlineshoppingandonlinebookbuyvalidatetheproporation,Usa,foundathttp://www.gvu.gatech.edu/user_surveys). posed framework. Table 6 lists the e-marketing terms that shop- Setting the buying scenario for general online shopping and online rs may perceive as important. Each term has a number for a book purchasing can gather generic and specific information about corresponding e-marketing strategy. search behavior Table 4 Cross-framework between frameworks of e-marketing strategies and shopper different buys. CMEC Promotion Proces eting strategies and different types of buys eral-purpose buy S3: Value buy √ √√√√√ 66: Service-oriented buy Table 5 The cross-framework between e-marketing strategies and shoppers' decision-making styles. Place Promotion Process S1(03)Local web site (o2)Preferred (o1)The availability (08. 1-8.5)Today s (O8.6)List (13. 1)Online payment of preferred product time-limited sale/ bundle sale/ price/(OS.7) nly deals/special offers/ (04 Contact information (o15)Friendliness 014 With order ecking feature (o5)With physical shop 012)Recommended produc assortment of (O7)With privacy and security availability of the (o11) New prod tact information (o1)The availability (OS)Seasonal sales (O8.6)Sale (014)with order checking feature (O7) With privacy and security assortments of availability of the recommendation sign 3(03)Web site that is located in (o1)The availability (oS)Special offers (o17)International (O11)Ne 010)Special 022)The availability of the The av ce design (o18)With gift service/ gift coupo S6 (020) With (o19)The availability (o14)With order of FAQ section checking feature (016) Customer services
information about price, a product picture, promotion news and online payment options. While looking for this information, shoppers make a buying decision based on these e-marketing terms. 2.5. General online shopping and online book shopping General online shopping and online book buy validate the proposed framework. Table 6 lists the e-marketing terms that shoppers may perceive as important. Each term has a number for a corresponding e-marketing strategy. 3. Research method 3.1. Method General online shopping and book shopping are chosen for this study to test shoppers’ search and decision-making behavior. Books are the most popular products over the Internet, as shown in GVU’s WWW User Survey (Source: Georgia Tech Research Corporation, USA, found at http://www.gvu.gatech.edu/user_surveys). Setting the buying scenario for general online shopping and online book purchasing can gather generic and specific information about search behavior. Table 4 Cross-framework between frameworks of e-marketing strategies and shopper different buys. Place CMEC Product Promotion Price Process E-marketing strategies and different types of buys S1: General-purpose buy pp p p pp S2: Security-concerned buy p p p p p S3: Value buy p p p p S4: Fashionable buy p p S5: Time-sensitive buy p p p S6: Service-oriented buy p p p Table 5 The cross-framework between e-marketing strategies and shoppers’ decision-making styles. Place CMEC Product Promotion Price Process S1 (O3) Local web site (O2) Preferred language (O1) The availability of preferred product (O8.1–8.5) Today’s special/ time-limited sale/bundle sale/ online-only deals/special offers/ discounts (O8.6) List price/(O8.7) sale price (13.1) Online payment option (O4) Contact information (O15) Friendliness of interface design (O18) With gift service/gift coupon (O9) Free shipping (O14) With order checking feature (O5) With physical shop (O6) Good assortment of products (O12) Recommended product (O7) With privacy and security policy (O20) The availability of the product’s picture (O11) New product (O12) Seasonal sales S2 (O4) Contact information (O1) The availability of preferred product (O8) Seasonal sales (O8.6) Sale price (O14) With order checking feature (O7) With privacy and security policy (O6) Good assortments of products (O21) With customer review, guarantee seal, or recommendation sign (O22) The availability of the product’s picture (O20) With return policy S3 (O3) Web site that is located in specific country (O1) The availability of preferred product (O8) Special offers (O17) International shipping S4 (O11) New products (O10) Special items (O22) The availability of the product’s picture S5 (O15) The availability of navigational/search tool, e.g. site map/ index: the friendliness of interface design (O12) Highlighted/ recommended products (O14) The availability of shopping cart/quick check-out (O18) With gift service/gift coupon S6 (O20) With return policy (O19) The availability of FAQ section (O14) With order checking feature (O16) Customer services 6878 W.-S. Lin et al. / Expert Systems with Applications 37 (2010) 6874–6884