Desires of an adopter's heart: which product characteristics influence brand loyalty among different types of adopters?
Maintaining customer loyalty has become a strategic mandate in today's competitive market (Ganesh et al., 2000; McMullan & Gilmore, 2008). Heskett et al. (1989) suggest that it costs three times as much to obtain a customer than to hold one. When companies increase 5% of their customer base, they increase their profit by 25-95% (Reichheld, 2001). Therefore, customer loyalty leads to a significant profit increase, more predictable sales and profit streams, and positive word- of-mouth (Arnould et al., 2002; Hoisington & Naumann, 2003).
Value signifies an important element of managing long-term customer relationships (Pride & Ferrell, 2010). Traditionally, value can be classified according to its relative utilitarian and hedonic nature (Babin et al., 1994; Bridges & Florsheim, 2008; Chandon, Wansink, & Laurent, 2000; Homer, 2008). Utilitarian value can help customers maximize the utility, efficiency, and economy of products. Hedonic value is more subjective and personal, and results more from fun and playfulness than from task completion (Chandon, Wansink, & Laurent, 2000). Products can be tentatively classified as utilitarian due to their definition, e.g., affordable price, high quality, complexity, convenience, and ease of use (Chandon, Wansink, & Laurent, 2000; Monroe & Lee, 1999; Monsuwe, 2004). In contrast, products have hedonic value if they are entertaining, fun, novel, intrinsically rewarding, and related to emotions and pleasure (Chandon, Wansink, & Laurent, 2000; Lim & Ang, 2008; Monroe & Lee, 1999). Previous studies (Andreassen & Lindestad, 1998; Chandon, Wansink, & Laurent, 2000; Chiu et al., 2005; Monsuwe, 2004; Pura, 2005; Ryu, Han, & Jang, 2010) indicate that utilitarian and hedonic values impact customer loyalty, which leads us to believe that customer perceptions of these benefits may increase their loyalty.
This paper focuses on technology products, software in particular, for the following reasons. First, although the computer software industry is growing rapidly and is increasingly critical for the international competitiveness of other high- technology industries, it has received relatively little attention from previous scholars (Mowery, 1995). Second, the software industry, as part of the high-tech industry, possesses three important characteristics: market uncertainty, technology uncertainty, and competitive volatility (Mohr, Sengupta, & Slater., 2005). For example, Burruss and Kuettner (2003) indicate that typical electronic consumer products have a life cycle ranging from nine to 18 months. Such an uncertain environment and short product life cycle make it critical for companies to formulate an effective customer loyalty strategy (Mohr, Sengupta, & Slater, 2005). Finally, in such a competitive environment, Moen, Gavlen, and Endresen (2004) suggest that a software company must focus on enhancing value in order to establish customer relationships. Therefore, maintaining customer relationships by providing value has become crucial for marketers in this industry.
In the innovation diffusions theories, although originally based on a study of agricultural innovations, Rogers' (1983) framework on the diffusion of innovations has provoked similar research within the fields of software (Prescott & Conger, 1995; Kautz & Larsen, 2000). Therefore, it is of great interest to determine the characteristics of customers within each adopter category (Brown & Duguid, 1991; Burruss, 2003; Haggman, 2009; MacVaugh & Schiavone, 2010; Martinez, Polo, & Flavian, 1998; Moore, 1999; Ozdemir & Trott, 2009). Despite the importance of maintaining customer relationships, almost none of the previous studies discuss the impact of product benefits on customer loyalty among different adoption categories of software. In the software industry, because each adopter segment is likely to perceive innovation attributes differently (Saaksjarvi, 2003), software marketers need to identify the different product benefits among multiple adopter categories before launching new products.
The purpose of the study is to investigate the impact of product benefits--price, ease of use, quality, novelty, and entertainment value--on customer loyalty among different adopter categories in the software market. We hypothesize that these product benefits impact customer loyalty in each adopter category. In the following sections, we review previous studies about consumer characteristics (customer loyalty, value, and product benefits) in each adopter category, and then present the research method to test our hypotheses. Finally, we provide a conclusion and discuss managerial implications.
LITERATURE REVIEW AND HYPOTHESES
Characteristic of Customers in each Adopter Category
The adopter categories discussed in the traditional adoption and diffusion models include technology enthusiasts, visionaries, pragmatists, conservatives, and skeptics (e.g., Mahajan, Muller, & Srivastava, 1990; Mohr, Sengupta, & Slater, 2005; Moore, 1999; Rogers, 1995).
Technology enthusiasts: Technology enthusiasts are innovators and are usually the first to try a new product or innovation (Rogers, 1983; Macri, Tagliaventi, & Bertolotti, 2001; Mohr, Sengupta, & Slater, 2005). They are often viewed as the opinion leaders (Macri, Tagliaventi, & Bertolotti, 2001; Martinez, Polo, & Flavian, 1998). They account for only 2.5% of consumers.
Visionaries: Visionaries belong in the early adopter market and are interested in using the new product. They are the first constituency who can and will bring real money to the table and are enthusiastic about the new functions that new products bring (Moore, 1991; Mohr, Sengupta, & Slater, 2005). They account for 13.5% of consumers.
Pragmatists: Pragmatists are customers in the early majority and make the bulk of all technology infrastructure purchases. They seek out tangible productivity enhancements (Mohr, Sengupta, & Slater, 2005). When they are struggling with the decision of adopting a new technology, they will consult those they trust for advice (Mohr, Sengupta, & Slater, 2005; Rogers, 1995). They account for 34% of market share.
Conservatives: Conservatives are customers in the late majority. The late majority conservatives are risk-averse and technology-shy and need completely surefire solutions (Mohr, Sengupta, & Slater, 2005). Although the market tends to be saturated and the profit is less, the late majority still account for 34% of market share (Mahajan, Muller, & Srivastava, 1990).
Skeptics: Skeptics represent the last segment of the technology adoption cycle and are technology laggards who want only to maintain the status quo. They account for 16% of market share (Mahajan, Muller, & Srivastava, 1990).
However, previous studies of consumer innovativeness were based on the time of adoption, when the diffusion process forms a normal distribution curve and is typical of the product lifecycle (Rogers, 1983; McCarthy, O'Sullivan, & O'Reilly, 1999; Rogers & Shoemaker, 1971). In high-tech industries, the product life cycle is generally much shorter, even shorter than many fashion products (Burruss & Kuettner, 2002). Therefore, it is more difficult to identify customers in each of the five adopter groups.
Due to concern about the size of the categories and the difficulty of distinguishing individuals within each category, many previous papers classify the potential adopters of an innovation according to two or three categories (e.g., Martinez, Polo, & Flavian, 1998; Pessemier, Burger, & Tigert, 1967). For example, Danko and MacLachlan (1983) identified a number of demographic and sociographic variables that differentiated early versus late adopters of PCs. Greco and Fields (1991) studied two categories: early adopters and late adopters for home video ordering systems. In the software industry, Hess (2005) also divides the software life cycle into two stages, and discusses how aligning techniques are applied to two stages. Thus, this study divides the diffusion process into two adopter categories in the software industry: early and late adopters.
The early adopters consist of three categories of customers: technology enthusiasts, visionaries, and pragmatists. This group accounts for the first 50% of customers and the majority of them are pragmatists (34% of the whole market). In this early stage, the price of the product is still higher because companies need to recoup their R&D costs, and the product quality and new features both need to be improved (Armstrong & Kotler, 2011). In contrast, the late adopters include two groups: conservatives, and skeptics. In this later stage, the price is usually lower and the product extensively developed.
High loyalty leads to higher market share because more customers are retained (Hoisington and Naumann, 2003). Kotler and Keller (2005) indicate that "based on a 20-80 principle, the top 20% of customers may create 80% of profit for a company." In general, loyalty includes both readiness to repeat purchase and resistance to alternatives (Arnould et al., 2002). Customer loyalty is a buyer's overall attachment or deep commitment to a product, service, brand, or organization (Oliver 1999). Raphel and Raphel (1995) classify customer loyalty according to five levels: prospect, shopper, customer, client, and advocate, with advocate representing the highest degree of loyalty. Repeat buyers are just clients until they recommend the product to others, which makes them advocates. If the customers are highly satisfied and loyal, these customers are more likely to generate word-of-mouth referrals (Reichheld & Sasser, 1990; Hoisington & Naumann, 2003). Therefore, this study defines that loyalty as including both repeat purchase and referrals.
Customer Perceptions of Product Values
Value is an important element of managing long-term customer relationships. Most researchers divide product values into two different categories: utilitarian and hedonic (Babin, Darden, & Griffin, 1994; Bridges & Florsheim, 2008; Chandon, Wansink, & Laurent, 2000; Homer, 2008; Mano & Oliver, 1993; Stoel, Wickliffe, & Lee, 2004). As with other consumer behavior concepts, these two types of value are useful in describing shopping rewards (Babin, Darden, & Griffin, 1994). In addition, value is a key concept to keep in mind as one evaluates new products (Harmancioglu, Finney, & Joseph, 2009; Mukherjee & Hoyer, 2001).
A product's utilitarian value can generally be stated in terms of price, cost saving, quality, function, ease of use, and convenience (Chandon et al., 2000; Mathwick et al., 2001; Monroe & Lee, 1999; Monsuwe, 2004). In contrast, the entertainment value and novelty can be tentatively classified as hedonic values, because they are intrinsically rewarding and related to emotions and pleasure (Chandon, Wansink, & Laurent, 2000; Mathwick, Mallhotra, & Rigdon, 2001; Monroe & Lee, 1999).
Monroe (2003) states that within the economic context, price is the amount of money customers must sacrifice to acquire something they desire. He further states that pricing a product or service is one of the most vital decisions a company makes. Consumers often haggle over price (Arnould, Price, & Zinkhan, 2002). Price can influence internal reference prices and product evaluations and is an important factor in determining whether a customer will re-purchase and/or recommend (Monroe & Lee, 1999). Therefore, product price has an impact on loyalty.
According to an economics theory, demand-curves are usually concave to the origin and slopes are usually negative. The arc elasticity becomes larger with dropping price and quantities of demand increase. To gain more market share, companies will consider competitors' actions and the price will fall in the early phase (Pride & Ferrell, 2010). Armstrong and Kotler (2011) suggest that firms lower prices at the right time to attract more buyers. For example, Apple attracted early adopters successfully with the iPhone at a price of $599 for an 8GB. Six months later, it dropped the price to $399. This indicates a company can lower prices significantly to attract more customers in the early stage, because price is usually high at the time of the product launch. However, in the later stage, although customers are sensitive to price (Mohr, Sengupta, & Slater, 2005), the price has already reached an acceptable level and the company should not lower a price significantly. Therefore, we propose:
H1: Price has a greater positive impact on customer loyalty among early adopters than among late adopters
Ease of Use
"Ease of use" is defined as the individual's perception that using the new technology will be free of difficulty or complications (Davis, Bagozzi, & Warshaw, 1989). Some products give consumers pleasure through convenience. In previous studies, ease of use has a direct or an indirect effect on consumers' intention to use a new technology (Monsuwe, 2004) and complexity tends to distract users. According to a recent study, ease of use is the top factor considered by respondents when acquiring new technology (Yang & Gonzalez, 2006). As the ease of use of the product increases, the attitude toward the product thereby grows more positive (Ajzen & Fishbein, 1980; Childers et al., 2001; Karjaluoto, Mattila, & Pento, 2002). Ease of use therefore impacts loyalty.
According to customer characteristics within different adopter categories, the late majority of conservatives (Moore, 1999; Mohr, Sengupta, & Slater, 2005) need completely preassembled, "bulletproof solutions. The conservative strategy is to stick with the old technology for as long as possible because it is familiar and functional (Moore, 2000). PC-cilline CTO Chen (http://www.trend.org/) suggests that a firm should provide more convenient products to customers in the late adopter group. Armstrong and Kolter (2011) also suggest that companies should develop convenience products to maintain customers or attract new users in the late adopter group. Therefore, we propose:
H2: Ease of use has a greater positive impact on customer loyalty among late adopters than among early adopters
Using the user-based approach, Sebastinelli and Tamimi (2002) define quality as "the extent to which a product or service meets and/or exceeds customers' expectations." Quality directly impacts product or service performance. For example, a number of stereo users remarked that they were satisfied by the particularly high sound quality of their systems. Thus quality is closely linked to customer value and satisfaction and many companies create product value by consistently meeting customers' needs and quality preferences (Armstrong & Kotler, 2011).
Quality can also be used to predict customer feelings (satisfaction) or buying behavior (Olsen, 2002). Bloemer and Ruyter (1998) indicate that quality is one of the elements of store image and impacts customer loyalty. Hellier, Geursen, and Richard (2003) indicate that the customer's re-purchase intention is greatly influenced by product quality. Therefore, quality has an impact on customer loyalty.
When customers are in the product introduction stage, they are more capable of learning new product features due to their acquired information and knowledge of the new products (Moore, 1999; Mohr, Sengupta, & Slater, 2005). Therefore, new features may have a greater impact on customer loyalty in the early stage of the product life cycle. Similarly, Armstrong and Kotler (2011) suggest improving product quality and new product features in the early stage. Therefore, we propose:
H3: Product quality has a greater positive impact on customer loyalty among early adopters than among late adopters.
Novelty is an innate human preference, which belongs to one of the hedonic values (Wood, 2005). Varied, novel, and surprising stimuli can elicit sensory curiosity (Bianchi, 1998). Novel stimuli arouse the curiosity of readers and entice them to examine the catalog information further (Stell & Paden, 1999). Therefore, novelty is conceptualized as the opposite of familiarity, and is associated with a lack of experience (Berlyne, Craw, & Salapatek, 1963).
Novelty is a stimulus property that can activate arousal and lead to exploratory behavior (Raju, 1980). The search for novelty often occurs when an individual becomes basically satisfied with products that they currently own (Stell & Paden, 1999). To remain competitive, it is increasingly important for firms to frequently introduce new/changed products (Raman & Chhajed, 1995; Sanderson & Uzumeri, 1997; Danese & Romano, 2004). Armstrong and Kotler (2011) and Mohr, Sengupta, and Slater (2005) indicate that in the later stage of the product life cycle, the company needs to modify the product--changing characteristics such as style and color of products to attract new users. Therefore, we propose:
H4: Novelty has a greater positive impact on customer loyalty among late adopters than among early adopters.
Babin, Darden, and Griffin (1994) note that the subjective and personal nature of intrinsic value perceptions results from the "fun and playfulness" of using the product, rather than from the task completion itself. Playful exchange behavior is reflect ed in the intrinsic enjoyment that comes from engaging in activities that are absorbing, to the point of offering an escape from the demands of the day-to-day world (Huizinga, 1995). "Pleasure" is the degree to which a person feels good, joyful, happy, or satisfied in online shopping (Monsuwe, Dellaert, & de Ruter, 2004). The element of fun has a restorative capability and operates outside of immediate material interests (Day, 1981). Playfulness is more evident in a one-to-one interaction and manifests itself as joy, sense of humor, and active involvement (Taylor, 1992).
Enjoyment results from fun and playfulness (Monsuwe, Dellaert, & de Ruter, 2004). Childers et al. (2001) found "enjoyment" to be a strong predictor of customer attitude. According to Mohr, Sengupta, and Slater (2005), a firm should not add additional interesting features and cool "wow" factors to products aimed at the late majority; instead, only provide the simpler components to this category. Therefore, we propose:
H5: Playfulness has a greater positive impact on customer loyalty among early adopters than among late adopters.
Procedures and Sample
A survey using a convenience sampling method was conducted in Taiwan. To provide results that we can interpret at a theoretical level, this study encompasses a wide range of software including anti-virus software, translation software, gaming software, and video software. In this study, all respondents were asked to choose one type of software s/he had used and circled their perception about it. Three hundred sixty-eight questionnaires were distributed and two hundred thirty questionnaires were deemed useful. The response rate was 64.7%.
On the basis of previous studies, we developed related measures shown in Table 1.
Based on the definitions of repeat purchase and referrals in current literature (Arnould, Price, & Zinkhan, 2002; Hoisington & Naumann, 2003; Oliver, 1999), we developed items to measure customer loyalty. Four statements were used to measure customer loyalty: "I intend to re-purchase the company's product"; "I would suggest this product to others"; "Even if the price increases, I will continue to purchase the company's product"; and "Even if a competitor lowers the price of their product, I will continue to purchase this company's product."
In addition, cluster analysis was used to categorize consumers on the consumer's innovativeness measure. Grouping was done on the basis of similarities or distances (dissimilarities). The inputs required are similarity measures or data from which similarities can be computed (Johnson & Wichern, 2002).
Rogers' five categories of adopters are built on innovativeness. He defines innovativeness as the "degree to which an individual is relatively early in adopting an innovation than other members of his system" (Rogers & Shoemaker, 1971). According to the definition, researchers usually take a certain amount of time to measure consumer innovativeness (Goldsmith & Hofacker, 1991). Hence, adapting from Goldsmith and Hofacker's (1991) study, this research used a total of 11 items to measure consumer innovativeness. The items that measure consumers' adoption of innovation phases/stages are "I am the first among my friends to buy any new software when it is on the market"; "I intend to buy the software"; "I like to use new software"; "I have all kinds of software"; "I will ask friends with the new software to allow me to test it"; "Among my friends, I am always the first to know the title of any new software"; "I like to use software in general"; "Even if I have not heard of new software, I will still consider buying it"; "I am the first one among my friends to know the name of the software"; "I love any software which is new and interesting"; "I like to buy any type of software."
In this study, a K-means method for clustering was used to analyze the respondents. When respondents are divided into several adopter phases/groups, the numbers in each group are small. According to the technology adoption life cycle concept, the early phase/group and the later phase/group each held one half of consumers. Therefore, in this study, we placed the respondents into two groups. The higher grade means that the subject has higher intention to adopt the new technology. After analysis, the result was 111 for early adopters (48.2%) and 119 for later adopters (51.8%). Therefore, the assumption that each phase/group held about one half of the subjects was supported.
ANALYSIS AND RESULTS
Reliability and Construct Validity
To examine the reliability of the scales of each construct, we computed Cronbach's alphas for the scales. The alphas were .91, .86, .85, .93, and .96 for price, ease of use, quality, novelty, and playfulness. In addition, the reliability of customer loyalty and consumer innovativeness (to measure which phase where customers are in) were .81 and .89. Those values show a high internal consistency in every dimension.
In order to examine construct validity, Churchill (1979) indicated that convergent and discriminant validities should be examined for construct validity. This study takes AVE (average variance extracted) to measure convergent validity with Lisrel 8.7. Convergent validity is supported when the average variance extracted (AVE) between the construct and their measures is greater than .50 (Fornell & Larcker, 1981). In this study, the AVEs for price, ease of use, quality, novelty, and playfulness are .83, .75, .73, .89, and .91, and all AVE exceed the level of .50. Thus convergent validity is supported.
Discriminant validity was demonstrated by chi-square difference tests in which a correlation between pairs of constructs is freely estimated and then set to equal 1 (Jap & Ganesan 2000). In Table 2, when setting the largest correlation of ease of use and quality equal to one, the chi-square differences between this new model and the original one was 59 (p<.05). Therefore, this study showed the discriminant validity.
Relationship between Product Benefits, Customer Loyalty, and Phases
To examine the impacts of the six determinants on customer loyalty for the early phase (early adopters) and the late phase (late adopters), this study calculated composite scores for each value by summing its items. These composite scores often are highly correlated with the factors scores obtained by the more complex regression method (Johnson and Wichern, 1992). Further, this study regressed two separate models which used customer loyalty as a dependent variable and the six product benefits as independent variables for the early phase and later phase. The variance inflation factors (VIF) values were much less than 10 (ranging from 1.17-2.02) and below the threshold suggested by Neter, et al. (1996); therefore, the effect of multicollinearity can be ignored.
As we show in Table 3, for early adopters, price and quality have significant positive impacts on customer loyalty (p<.05) and playfulness has a marginally positive impact on customer loyalty (p<.10). For later adopters, price has a positive impact on customer loyalty (p<.05) and ease of use has a marginally positive impact on loyalty (p<.10). This indicates that price is the most useful strategy to maintain customer loyalty in both the early and late phases; quality is beneficial to customer loyalty in the early adopters; playfulness is marginally helpful for customer loyalty in the early phase; and ease of use is marginally useful to maintain loyalty in the later phase.
To test the significance of differences in the regression coefficients between early and later phases, we used an omnibus test, the Chow (1960) test. This test assesses whether an overall difference exists in parameter values between groups but does not assess the significance of individual parameter estimates. In this study, the F value was 2.0 (p<.10), which indicates that a marginal difference exists in the coefficients of product benefits between early and later groups.
To examine further whether a specific product benefit has a greater positive impact on customer loyalty for a specific phase, we compare the unstandardized regression coefficients between product benefits and customer loyalty for early and later phases according to Arnold (1982, p.156). The results indicate the impact of the price on loyalty is significantly larger for those in the early phase than for those in the later phase (t=1.70, p<.05 under one-tailed test); and the impact of quality on loyalty is significantly larger for those in the early phase than for those in the later phase (t=1.90, p<.05 under one-tailed test). Conversely, the impact of ease of use on loyalty is marginally larger for those in the later phase than for those in the early phase (t=1.30, p<.10 under one-tailed test); the impact of playfulness on loyalty is marginally larger for those in the early phase than for those in the later phase (t=1.31, p<.10 under one-tailed test). Accordingly, H1 and H3 are supported (p<.05), and H2 and H5 are marginally supported (p<. 10) in this study.
Although the importance of maintaining customer relationships in the software industry is increasing, empirical research focusing on how product benefits impact customer loyalty among different adoption categories of software is relatively sparse. To link product benefits and customer loyalty in the software industry, the study uses two categories (early and late) of the adopter classification as moderators; price, ease of use, quality, novelty, and playfulness as the product benefits. The results indicate that price, quality, and playfulness are more important to early than late adopters. The results also show that price is important for any consumer, regardless of the adopter category. In addition to price, ease of use is more important to later than early adopters.
The short life cycle of software products (Burruss & Kuettner, 2002) and high number of competitors in the market have made customer loyalty a strategic mandate (Ganesh, Arnold, & Reynolds, 2000). Loyal customers purchase more frequently, are willing to spend more, are more accessible, and act as enthusiastic advocates (Harris & Goode, 2004). The results of our study provide some strategic implications for software companies to build their customers' brand loyalty.
First, software companies must understand that customer perceptions of product benefits include both utilitarian and hedonic benefits. Utilitarian benefits are related to price, quality, and ease of use; playfulness and novelty are classified as hedonic benefits. Although many product benefits exist, they can all be categorized into utilitarian and hedonic benefits in the software industry. To managers, understanding the full effects of these benefits on loyalty is critical to improving their customer relations.
Second, marketers can segment software consumers according to the diffusion stage in which they purchase, in order to communicate the appropriate product benefits in each stage. This study demonstrated that price, quality, and playfulness are more important to early adopters than late adopters. However, to achieve greater profitability from long-term relationships, it is critical to pay attention to price and ease of use as these qualities are more important to later adopters than early adopters. Hence, the results suggest that software companies need to emphasize the appropriate product benefits to build customer loyalty. By doing this software companies can increase their competitive advantage among different adopter groups.
Third, a software company needs to re-think its target when entering markets. Because different kinds of software are available in different diffusion stages, the company must adjust and pay close attention to target customers. For example, newly introduced software needs to have a high degree of quality and playfulness and a competitive price in order to attract early adopters. However, when marketing existing software to conservatives, simplicity and a lower price are the most important factors.
Limitations and Future Directions
There are four main limitations in this study, the first being that the study only includes software. According to Rogers (1995), technology consists both of software and hardware. In many instances software is less observable than the hardware part, but should nevertheless be taken into consideration when conducting technology research (Rogers, 1995; Saaksjarvi, 2003). In addition, software piracy is a prevalent and serious problem worldwide. Global spending on counterfeit, packaged PC software in 2006 was about $40 billion (Business Software Alliance, 2007). The wide availability of pirated software in the market may have a great impact on subjects' responses. The third limitation is the problem of external validity, i.e., the ability to generalize the results outside Taiwan. Finally, the sampling method for this study was a convenience sampling that was not scientifically designed. Therefore, significant effort should be devoted to detecting any potential biases in these nonrandom samples.
This study does provide direction for future study. First, some studies (Mano & Oliver, 1993; Babin, Darden, & Griffin, 1994; Spangenberg & Voss, 1997) developed scales to measure the degree of hedonic and utilitarian value to classify product attributes or experience of shopping in the past. In the software industry, several utilitarian and hedonic benefits seem to be statistically significant to customer loyalty, but the effects of these benefits in the hardware industry are unknown. Thus future studies can consider different high-tech hardware attributes to research the relationship of customer value (product benefits) and loyalty. Second, other high-tech industries such as laptop manufacturers, segment their customers based on how they use laptops. For example, students use laptops for school; salespeople for work; game players for games, and so on. Future studies can divide customers according to their purposes of using the product.
Third, in diffusion-of-innovation literature, adopter categories required certain methods to define them, which depended on the relative rate at which consumers adopt innovations (Mahajan, Muller, & Srivastava, 1990). In this study, we used clusters to categorize the consumers; future studies could investigate alternatives such as the cross-sectional. Fourth and finally, Hofstede (1980) proposes four dimensions of culture: power distance, uncertainty avoidance, individualism/ collectivism, and masculinity/ femininity. The primary characteristic of Chinese culture appears to be a more collectivistic orientation, whereas North American culture is typically characterized as individualistic. Can our results be applied into the other countries? Further research should extend our hypotheses to different cultures and compare the results with this study.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. New York, NY: Prentice-Hall.
Andreassen, T.W., & Lindestad, B. (1998). Customer loyalty and complex services: The impact of corporate image on quality, customer satisfaction and loyalty for customers with varying degrees of service expertise. International Journal of Service Industry Management, 9(1), 7-23.
Armstrong, G., & Kotler, P. (2011). Marketing: An introduction. Upper Saddle River, NJ: Prentice Hall.
Arnold, H. J. (1982). Moderator variables: A clarification of conceptual, analytic, and psychometric issue. Organizational
Behavior and Human Performance, 29(2), 143-174.
Arnould, E. J., Price, L. L., & Zinkhan, G. M. (2002). Consumers. New York, NY: McGraw-Hill.
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644-656.
Berlyne, D. E., Craw, M. A., & Salapatek, P. H. (1963). Novelty, complexity, incongruity, extrinsic motivation, and the GSR. Journal of Experimental Psychology, 66(6), 560-597.
Bianchi, M. (1998). The active consumer: Novelty and surprise in consumer choice. New York, NY: Routledge.
Bianchi, M. (1998). Consuming novelty: Strategies for producing novelty in consumption. Journal of Medieval and Early Modern Studies, 28(1), 3-18.
Bloemer, J., & de Ruyter, K. (1998). On the relationship between store image, store satisfaction and store loyalty. European Journal of Marketing, (32), 499-513.
Bridges, E., & Florsheim, R. (2008). Hedonic and utilitarian shopping goals: The online experience. Journal of Business Research, 61(4), 309-314.
Brown, J., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40-57.
Burruss, J., & Kuettner, D. (2002). Forecasting for short-lived products: Hewlett-Packard's journey. Journal of Business Forecasting Methods & Systems, 21(4), 9-17.
Business Software Alliance. (2007). 2007 worldwide software piracy study. Available at: http://w3.bsa.org/globalstudy
Chandon, P., Wansink, B., & Laurent, G. (2000). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing, 64(4), 65-81.
Childers, T.L., Carr, Christopher L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
Chiu, H. C., Hsieh, Y. C., Le, Y. C., & Lee, M. (2005). Relationship marketing and consumer switching behavior. Journal of Business Research, 58(12), 1681-1689.
Chow, G. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591- 605.
Churchill, G.A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73.
Danese, P., & Romano, P. (2004). Improving inter-functional coordination to face high product variety and frequent modifications. International of Operations & Production Management, 24(9), 863-86
Danko, W., & MacLachlan, J. (1983). Research to accelerate the diffusion of a new invention--the case of personal computers. Journal of Advertising Research, 23(3), 39-43.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Day, H. I. (1981). Advances in intrinsic motivation and aesthetic. New York, NY: Plenum Press.
Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000, July). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64, 65-87.
Goldsmith, R E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209-221.
Greco, A. J., & Fields, D. M. (1991, summer). Profiling early triers of service innovations: A look at interactive home video ordering services. Journal of Services Marketing, 5, 19-26.
Haggman, S. (2009). Functional actors and perceptions of innovation attributes: Influence on innovation adoption. European Journal of Innovation Management, 12(3), 386-407.
Harmancioglu, N., Finney, R. Z., & Joseph, M. (2009). Impulse purchases of new products: An empirical analysis. Journal of Product & Brand Management, 18(1), 27-37.
Harris, L.C., & Goode, M. M. H. (2004). The four levels of loyalty and the pivotal role of trust: A study of online service dynamics. Journal of Retailing, 80(2), 139-158.
Heskett, J. L., Sasser, W. E. Jr., & Hart, C. W. (1989). Service breakthrough. New York, NY: The Free Press.
Hellier, P. K., Geursen, G. M., & Richard, J.A. (2003). Customer repurchase intention: A general structural equation model. European Journal of Marketing, 37(11/12), 1762-1800.
Hess, H. M. (2005). Aligning technology and business: Applying patterns for legacy transformation. IBM Systems Journal, 44(1), 25-45.
Hoisington, S., & Naumann, E. (2003). The loyalty elephant. Quality Progress, 36(2), 33-41.
Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage.
Homer, P.M. (2008). Perceived quality and image: When all is not rosy. Journal of Business Research, 61(7), 715-723.
Huizinga, J. (1995). Homo ludens: A study of the play element in culture. Boston, MA: Beacon Press.
Jap, S. D., & Ganesan, S. (2000). Control mechanisms and the relationship life cycle: Implications for safeguarding specific investments and developing commitment. Journal of Marketing Research, 37 (2), 227-245.
Johnson, R. A., & Wichern, D. W. (1992). Applied multivariate statistical analysis. Englewood Cliffs, N J: Prentice- Hall.
Karjaluoto, H., Mattila, M., & Pento, T. (2002). Factors underlying attitude formation towards online banking in Finland. International Journal of Bank Marketing, 20(6), 261-272.
Kautz, K., & Larsen, E. A. (2000). Diffusion theory and practice: Disseminating quality management and software process improvement innovations. Information Technology & People, 13(1), 11-26.
Kotler, P., & Armstrong, G. (2012). Principles of marketing. Upper Saddle River, NJ: Prentice Hall.
Kotler, P., & Keller, K.L. (2005). Marketing management. Englewood Cliffs, NJ: Prentice-Hall.
Lim, E.A.C., & Ang, S.H. (2008). Hedonic vs utilitarian consumption: Across-cultural perspective based on cultural conditioning. Journal of Business Research, 61(3), 225-232.
Macri, D. M., Tagliaventi, M. R., & Bertolotti F. (2001). Sociometric location and innovation: How the social network intervenes between the structural position of early adopters and changes in the power map. Technovation, 21(1), 1-13.
MacVaugh, J., & Schiavone, F. (2010). Limits to the diffusion of innovation: A literature review and integrative model. European Journal of Innovation Management, 13(2), 197-221.
Mahajan, V., Muller, E., & Srivastava, R. (1990). Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research 27(1), 37-50.
Mano, H., & Oliver, R.L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451-466.
Martinez, E., Polo, Y., & Flavian, C. (1998). The acceptance and diffusion of new consumer durables: Differences between first and last adopters. Journal of Consumer Marketing, 15(4), 323-342.
Mathwick, C., Mallhotra, N., & Rigdon, E. (2001). Experiential value: Conceptualization, measurement and application in the catalog and internet shopping environment. Journal of Retailing 77(1), 39-56.
McCarthy, M., O'Sullivan, C., & O'Reilly, S. (1999). Pre-identification of first buyers of a new food product. British Food Journal, 101(11), 842-856.
McMullan, R., & Gilmore, A. (2008). Customer loyalty: An empirical study. European Journal of Marketing, 42 (9/10), 1084-1094.
Moen, O., Gavlen, M., & Endresen, I. (2004). Internationalization of small, computer software firms: Entry forms and market selection. European Journal of Marketing, 38(9-10), 1236-1251.
Mohr, J., Sengupta, S., & Slater, S. (2005). Marketing of high-technology products and innovations. Upper Saddle River, NJ: Prentice Hall.
Moore, G. A. (1991). The product adoption curve in crossing the chasm, marketing and selling technology products to mainstream customers. New York, NY: HarperCollins.
Moore, G. A. (1999). Crossing the chasm: Marketing and selling high-tech products to mainstream customers. Revised Edition. New York, NY: Harper Business.
Moore, G. A. (2000). Living on the fault line. New York, NY: Harper Business.
Monsuwe, T. P. Y., Dellaert, B.G. C., & de Ruter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102-121.
Monroe, K.B. (2003). Pricing: Making profitable decisions. New York, NY: McGraw-Hill.
Monroe, K. B., & Lee, A.Y. (1999). Remembering versus knowing: issues in buyers' processing of price information. Journal of the Academy of Marketing Science, 27(2), 207-225.
Mowery, D. C. (1995). The international computer software industry: A comparative study of industry evolution and structure. New York, NY: Oxford University Press.
Mukherjee, A., & Hoyer, W.D. (2001). The effect of novel attributes on product evaluation. Journal of Consumer Research, 28(3), 461-472.
Neter J., Kutner M. H., Nachtsheim C. J., & Wasserman W. (1996). Applied linear statistical models. Boston, MA: McGraw Hill.
Oliver, R. I., (1999) Whence consumer loyalty? Journal of Marketing, 63(special issue), 33-44.
Olsen, S. O. (2002). Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty. Journal of the Academy of Marketing Science. 30(3), 240-249.
Ozdemire, S., & Trott, P. (2009). Exploring the adoption of a service innovation: A study of Internet banking adopters and non-adopters. Journal of Financial Services Marketing, 13(4), 284-299.
Pessemier, E. A., Burger, P.C., & Tigert, D.J. (1967).Can new product buyers be identified? Journal of Marketing Research, 4(4), 349-354.
Prescott, M., & Conger, S. (1995). Information technology innovations: A classification by IT locus of impact and research approach. Data base for advances in information systems, 26(2-3), 20-41.
Pride, W. M., & Ferrell, O.C. (2010). Foundations of marketing. Mason, OH: South-Western Cengage Learning.
Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509-538.
Raju, P. S. (1980). Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research, 7(3), 272-282.
Raman, J., & Chhajed, D. (1995). Simultaneous determination of product attributes and prices, and production processes in product-line design. Journal of Operations Management, 12, 187-204.
Raphel, N., & Raphel, N. (1995). Up the loyalty ladder: Turning sometime customers into full-time advocates of your business. New York, NY: Harper Collins.
Reichheld, F. F. (2001). Loyalty rules: How today's leaders build lasting relationships. Boston, MA: Harvard Business.
Reichheld, F., & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105- 111.
Rogers, E. M. (1983). Diffusion of innovations. New York, NY: The Free Press.
Rogers, E. M. (1995). Diffusion of Innovations. New York, NY: The Free Press.
Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innovations. New York, NY: The Free Press.
Ryu, K., Han, H., & Jang, S. (2010). Relationships among hedonic and utilitarian values, satisfaction and behavioral intentions in the fast-casual restaurant industry. International Journal of Contemporary Hospitality Management, 22(3), 416-432.
Saaksjarvi, M. (2003). Consumer adoption of technological innovations. European Journal of Innovation Management, 6(2), 90-100.
Sanderson, S. W., & Uzumeri, M. (1997). Managing product families. New York, NY: Irwin McGraw-Hill.
Sebastianelli, R., & Tamimi, N. (2002). How product quality dimensions relate to defining quality. International Journal of Quality & Reliability Management, 19(4), 442-453.
Spangenberg, E. R., & Voss, K. E. (1997). Measuring the hedonic and utilitarian dimensions of attitude: A generally applicable scale. Advances in Consumer Research, 24(1), 235-241.
Stell, R., & Paden, N. (1999). Vicarious exploration and catalog shopping: A preliminary investigation. The Journal of Consumer Marketing, 16(4), 33-42.
Stoel, L., Wickliffe, V., & Lee, K.H. (2004). Attribute beliefs and spending as antecedents to shopping value. Journal of Business Research, 57(10), 1067-1073.
Taylor, S. I. (1992). The relationship between playfulness and creativity of Japanese preschool children. Virginia Polytechnic
Institute and State University, Ph.D. dissertation.
Wood, M. (2005). Discretionary unplanned buying in consumer society. Journal of Consumer Behaviour, 4(4), 268-281.
Yang, J., & Gonzalez, A. (2006). Key factors in buying tech items. USA Today, Feb. 16, p. B1.
Anurag Pant, Indiana University South Bend
Hung-Chang Chiu, National Tsing Hua University
Yi-Ching Hsieh, National Central University
Yi-Fan Huang, National Chung Hsing University
TABLE 1 Customer Value/Product Benefits Measurements Product Construct Definition Measure (Disagree/Agree) benefit Price "Price" is the amount of I can buy it with higher money customer must quality at the same price. sacrifice to acquire The price is reasonable. something customer desire I am satisfied with the (Monroe, 2003) price. Ease of use "Ease of use" is defined The product would be easy as the individual's to use. perception that using the The product would not new technology will be require too much effort. free of effort (Davis et The instructions for product al., 1989; Monsuwe, use would be clear and 2004). understandable. Quality The extent to which a The product is comparatively product or service meets better than others. and/or exceeds customers' The product has full expectations functionality. (Sebastinelli & Tamimi, The product is durable. 2002). Novelty "Novelty" is The product is novel. conceptualized as the The product incites opposite of familiarity, curiosity. and reflects a lack of I intend to explore the experience (Berlyne et product. al., 1963). Playfulness "Playfulness" is more The product is entertaining. evident in a one-to-one The product is fun. interaction and manifests The product is enjoyable. itself as joy, sense of humor, and active involvement (Taylor, 1992). TABLE 2 Correlations of Product Values Price Ease of use Quality Novelty Playfulness Price 1.00 Ease of use .30 1.00 Quality .43 .78 1.00 Novelty .14 .34 .36 1.00 Playfulness .18 .27 .34 .77 1.00 TABLE 3: Regression Coefficients in Each Phase Price Ease of Quality Novelty Playfulness H1 use H2 H3 H4 H5 Early adopters .770 ** -.008 .529 ** -.075 .213 * Late adopters .488 ** .348 * .032 .087 -.013 [R.sup.2] F Early adopters .51 21.5 ** Late adopters .19 5.3 ** * p<.10; ** p<.05
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|Author:||Pant, Anurag; Chiu, Hung-Chang; Hsieh, Yi-Ching; Huang, Yi-Fan|
|Date:||Jun 1, 2011|
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