J
Ö N K Ö P I N GI
N T E R N A T I O N A LB
U S I N E S SS
C H O O L JÖ N KÖ P I N G U N IVER SITYF a c t o r s A f f e c t i n g C o n s u m e r
R e s i s ta n c e t o I n n o v a t i o n
- A s t u d y o f S m a r t p h o n e s -
Master Thesis within Business Administration Author: Kamran Khan
Kim Hyunwoo Tutor: Desalegn Abraha Jönköping May 2009
Abstract
Background: In mobile phone industry, Smartphones are gaining popularity as an effective communication tool, providing users with “Smart” functionalities of both cell-phone and Personal Digital Assistant (PDA). Experts in mobile industry expect that smartphones are going to be dominant in mobile phone market. However, Smartphone industry is facing a different reality, with its declining sales and less market share, forcing research companies (Gartner, Canalys, etc.) to change their expectations. This situation leads us to another important and often ignored perspective of innovation challenges, i.e. consumers' resistance; as consumers' adoption and purchase decision makes a significant difference in the success of innovative products.
Problem: Innovation has been called as a key factor for companies to survive and grow in the long run, especially in the dynamic & complex markets and uncertain economic circumstances. Despite the successful outcome of innovations, inhibition or delay in the diffusion of innovation may translate this success into market failure, where resistance has been called as one of the main reasons for inhibiting or delaying the innovation diffusion. Consumers adoption of innovation depend upon several factors: the most important of which are specified as consumers’ characteristics (psychological characteristics of consumers; how they view the innovativeness with respect to that particular product), and the innovation characteristics (outcome and effects of innovation). Past research on innovation & consumers characteristics represents good relationship among the innovation/consumers factors and the adoption/implementation of that innovation by consumers.
Purpose: The purpose of this study is to identify and analyze the relationship between consumers' resistance and different factors from innovation and consumers' characteristics. Thereafter, important factors are identified that mainly affect/determine consumers' resistance to smartphones. Moreover, the inter-relationship (correlation) among the selected factors is found out, to know the affects of each factor on other factors.
Method: Following abductive approach, confirmatory factor analysis has been done on pre-test questionnaires to test, improve, and verify the constructs (variables/questions) for measuring the hypothesized factors. A theoretical model has been proposed from the hypotheses; and Structural Equation Modeling has been applied, where results are estimated through Partial Least Square and AMOS approaches, using a sample of 330 respondents from Sweden. SmartPLS software has been used to estimate results, thereafter, AMOS has been used to check and verify the results. Almost same results have been derived from both approaches, while results from PLS are found as more satisfactory.
Conclusions: Five out of eight hypotheses have been supported by our empirical data, where H1 i.e. relative advantage, H3 i.e. complexity, and H4 i.e. perceived risk, are from innovation characteristics, while H6 i.e. motivation, and H7 i.e. „favorable attitude towards existing products‟ are from consumers' characteristics. Motivation, Complexity, Relative Advantage, and Perceived Risk are found as important factors (as per their order) that affect/determine consumers' resistance to smartphones. Relative Advantage & Motivation are found as positively correlated, and Perceived Risk & Complexity are found as positively correlated. Negative correlation has been found between Perceived Risk and relative advantage. Similarly, negative correlation has been found between motivation and complexity. The proposed model of consumers resistance to smartphones shows an acceptable goodness of fit, where 65% (R-square value) of variation in consumers resistance is caused/explained by the hypothesized factors.
Acknowledgement
Thesis writing is always a great source of learning and experiences, which cannot be done only with one owns efforts, but also dependent on tremendous help from supervisor, faculty members, friends, and family. First, we would like to express our deepest gratitude to our supervisor Desalegn Abraha, who provided us guidance, critical evaluation and constructive feedback throughout the process of thesis writing. Our sincere thanks go to Andreas Stephen (faculty member), who discussed and explained different statistical methods/tools to achieve the objectives of this study.
We are thankful to all those friends who helped us improve, distribute, and respond to the questionnaires. Our colleagues within the seminar group remained a good source of critical feedback and helpful ideas/suggestions that made this work interesting and also challenging for us. Last but not the least; we would like to thanks our family members for their patience, encouragement, and support to complete this study.
Kamran Khan & Kim Hyunwoo
Jönköping International Business School, Sweden May, 2009
Table of Contents
1
Introduction ... 6
1.1 Background ... 6 1.2 Problem discussion ... 7 1.3 Research questions ... 10 1.4 Purpose ... 10 a. Hypothesis ... 10b. Regression coefficients and Correlation ... 11
1.5 Delimitations ... 11
2
Frame of reference ... 12
2.1 Innovation ... 12 2.1.1 Technological Innovation ... 12 2.1.2 Types of Innovation ... 12 2.2 Smartphone ... 13 2.3 Innovation Resistance ... 13 2.4 Sheth Model ... 15 2.5 Ram’s Model ... 162.6 Yu and Lee Model ... 17
2.7 Technological Acceptance Model (TAM) ... 17
2.8 Related studies ... 18
2.9 Factors Affecting Consumers’ Resistance ... 19
2.9.1 Innovation Characteristics Factors ... 20
2.9.2 Consumers Characteristics Factors ... 23
2.10 Hypotheses formulation ... 25
2.10.1 Relative Advantage ... 26
2.10.2 Compatibility ... 26
2.10.3 Complexity ... 26
2.10.4 Perceived Risk ... 26
2.10.5 Expectation for better products ... 26
2.10.6 Motivation ... 27
2.10.7 Attitude towards existing products ... 27
2.10.8 Self-Efficacy ... 27
2.11 Theoretical Model of Consumers Resistance to Smartphones ... 28
3
Method ... 29
3.1 Research Philosophy ... 29 3.2 Research Approach ... 30 3.3 Research Method ... 31 3.4 Research Strategy ... 31 3.5 Data Collection ... 31 3.6 Sampling ... 323.7 Data Analysis and Tools ... 33
3.8 Statistical Methods ... 34
3.8.1 Factor analysis ... 34
3.8.2 Hypothesis testing ... 35
3.9 Trustworthiness of the Research ... 36
3.9.2 Source of Empirical data ... 37
3.9.3 Approach followed to derive results ... 37
4
Empirical Findings ... 38
4.1 Preliminary Analysis ... 38
4.1.1 Confirmatory Factor Analysis ... 38
4.1.2 Consistency (Reliability) Analysis ... 40
4.1.3 Variables Operationalization & Designing questionnaire ... 41
4.2 Descriptive Findings ... 42
5
Analysis ... 43
5.1 Testing Hypotheses ... 43
5.1.1 Partial Least Square ... 43
5.1.2 AMOS ... 44
5.1.3 Results Discussion ... 45
5.1.4 Regression Equation ... 48
5.2 Factors Inter-relationship (Correlation) ... 48
6
Conclusion ... 50
6.1 Suggestions for further research ... 51
7
References ... 52
8
Appendix ... 60
8.1 Pre-test Questionnaire ... 60
8.2 Graphical Representation of factors and measuring variables ... 60
8.2.1 Relative Advantage ... 60
8.2.2 Compatibility ... 60
8.2.3 Complexity ... 61
8.2.4 Perceived Risk ... 61
8.2.5 Expectation for better Products ... 62
8.2.6 Motivation ... 62
8.2.7 Attitude towards Existing Products ... 63
8.2.8 Self-Efficacy ... 63
8.2.9 Consumer Resistance ... 64
8.3 Factor Loadings from the final empirical data ... 65
List of Figures & Tables
1 Figure 2.1 ... 14 2 Figure 2.2 ... 15 3 Figure 2.3 ... 16 4 Figure 2.4 ... 17 5 Figure 2.5 ... 28 6 Figure 5.1 ... 43 7 Figure 5.2 ... 44 8 Figure 5.3 ... 45 1 Table 4.1 ... 38 2 Table 4.2 ... 41 3 Table 4.3 ... 41 4 Table 4.4 ... 42 5 Table 5.1 ... 46 6 Table 5.2 ... 47 7 Table 5.3 ... 49List of abbreviations used
CR= Consumers Resistance RELADV = Relative Advantage COMP = Compatibility
CLEX= Complexity PRISK= Perceived Risk
EXPBPR= Expectations for Better Products ATEXPR=Attitude toward Existing Products MOTIV= Motivation
SE= Self-Efficacy
SEM = Structural Equation Modeling CFA= Confirmatory Factor Analysis EFA= Exploratory Factor Analysis PLS= Partial Least Square
AMOS= Analysis of Moment Structures PDA= Personal Digital Assistant
SPSS= Statistical Package for Social Sciences
Notes
The terms, Innovation resistance and consumers' resistance have been used interchangeably. Similarly the terms like; consumers, respondents, and users are used interchangeably.
1
Introduction
This chapter aims to introduce the reader to the background of the study topic, the problem area and a brief discussion of related studies. The research questions are formulated, followed by the purpose, hypothesis, and delimitation of this study.
1.1 Background
The trend in mobile phone innovations is going toward a mobile device integrating all the consumer electronic products, like MP3, Camera, Internet (Computer), GPS, and even TV. A new buzzword “SMARTPHONE” (marketing-friendly tag) represent this well known PDA-Cell phone combination (PDA-Phone combo) with manifold functions, representing a radical innovation in mobile phone industry (Park & Chen, 2007). Smartphones are excellent communication tools, providing users with “smart” functionalities of both PDA (Personal Digital Assistants) & cell phones (Nanda et al., 2008). These devices have become an important part of users' life, as they are not only communication tools but also expressions of their lifestyle (Castells, 2006) providing impressive usable interface (Monk et al. 2002). Smartphones are more powerful, with increasing processor capability and storage space, and enhanced communication & multimedia functions (Nguyen et al., 2008).
Experts in mobile industry expect that smart-phones are going to be dominant in mobile phone market. However, Smartphone industry is facing a different reality, and the statistics of current market show very less percentage of smart-phone users against traditional/old mobile phone users. According to Gartner, proportion of Smartphone in mobile phone market was at 12 per cent in the fourth quarter of 2008, from 11 per cent in the fourth quarter of 2007. Research companies, who expected explosive growth of Smartphone, are changing their expectations. In March 2009, Gartner said that increase in Smartphone sales started to slow down (Gartner, 2009).
The general perception is that high price is the main reason for Smartphones' low market shares (Martin, 2007). However, there is continuous downfall in the prices of Smartphones, and most are available for 200USD (CNET, 2009) which is almost equal to the price of normal mobile phone. Only 1% increase in smart phone market share from 2007 to 2008 and considerable price decrease shows that price remained a least important factor inhibiting the adoption. Smartphone manufacturers may not increase the market shares by simply reducing the prices, as price itself cannot be the main reason for its low market shares.
This situation leads us to another important and often ignored perspective of innovation challenges, i.e. consumers‟ resistance. As Smartphones represent “radical innovation” which face considerably more consumers' resistance than “incremental innovation” (Garcia et al., 2007, Heiskanen et al., 2007). Consumer (or end user) adoption and purchase decision make a significant difference in the success of innovative product, so consumers' resistance is one of the important factors in the success of innovation. It can certainly inhibit and/or delay the diffusion of an innovation and thus has important implications for the management of firms (Bradley & Stewart, 2002). Consumers, who resist innovation, are most of the time non-adopters and represent a major part of consumers. These consumers have strong potential for providing valuable information
necessary for the development, implementation, and marketing of innovation, and should be given more attention in research studies (Laukkanen et al., 2008).
From managerial perspective, studying consumers' resistance to innovation is very important and useful. Understanding resistance will help firms design/develop new products so as to ensure market success, and high product failure rate that is prevalent today, can be reduced. Once firms face consumers‟ resistance to their innovations, they can analyze the underlying causes of resistance, and better be able to design strategies to deal with critical & important resisting factors (Ram, 1987). Studying the factors affecting consumer resistance to Smartphones can provide its manufacturers/marketers with useful information about these important factors that affects consumers' behavior towards innovation.
1.2 Problem discussion
Innovation is a key factor for companies to survive and grow in the long run (Tidd, 2001), and has been called as the lifeblood of most organization (Balachandra & Friar 1997) especially in the dynamic & complex markets, and uncertain economic circumstances (Assink, 2006). Despite the successful outcome of innovations, inhibition or delay in the diffusion of innovation may translate this success in to market failure (Gatignon & Robertson 1991, Crawford 1983, Mahajan et al. 2000). One of the main reasons for inhibiting or delaying the innovation diffusion is consumers‟ resistance, which appears to have been neglected in the academic literature (Ram 1989, Ram & Sheth, 1989) (Laukkanen et al., 2008, Kuisma et al., 2007).
Even though the innovative product may provide extensive benefits and improved functionalities, researchers have found that consumers often convey less than enthusiastic response to a number of new products (Gold, 1981; Brod, 1982; Murdock & Franz, 1983; Blackler & Brown, 1985; Salerno, 1985; O'Connor et al., 1990). This response is most usually not expected (Heiskanen et al., 2007) and is expressed in a number of forms, but is usually termed as consumers' resistance (Ellen & Bearden, 1991). Consumers' resistance has been defined as “Innovation resistance is the resistance offered by consumers to an innovation, either because it poses potential changes from a satisfactory status quo or because it conflicts with their belief structure” (Ram & Sheth, 1989, p. 6). The previous research findings imply that firms introducing new products/innovations are required to take consumers' resistance more seriously (Heiskanen et al., 2007).
Consumers' resistance plays an important role in the success of innovation, as it can certainly inhibit or delay the consumer adoption. It has been termed as one of the major causes for market failure of innovations (e.g. Ram 1989, Ram & Sheth 1989, Sheth 1981) and also a valuable source of information vital to the successful implementation and marketing of innovation (O'Connor et al., 1990). If the resistance cannot be broken down, adoption slows down, and the innovation is likely to fail (Ram 1989). Firms need to understand consumers resistance, its reasons, and influencing factors; in order to become much more efficient in their improvement efforts, and to identify ways to improve competitiveness, productivity, and profitability (Dunphy & Herbig, 1995). Consumer adoption of innovation depends upon several factors: the most important of
consumers; how they view the innovativeness with respect to that particular product), and the innovation characteristics (outcome and effects of innovation) (Dunphy & Herbig, 1995, W. Robert, 1998). Innovation characteristics research represents the relationship among the attributes or characteristics of an innovation and the adoption, use, or implementation of that innovation (Tornatzky & Klein, 1982).
Robert (1998) argues that, there is a need to identify and understand the factors that seem to most influence customers' resistance to innovative products. It is important to study the effects of important factors (related to consumers' characteristics and innovation characteristics) on consumers' resistance that will reveal the importance of each factor, the intensity of their effect and their relationship. On the other hand, understanding the key factors of consumers and innovation characteristics that affect customers' resistance is crucial for firms' project team to improve its chances of making the right decisions throughout the design and development efforts (W. Robert, 1998). Ram (1987, 1989) argues that, the reasons for innovation resistance vary across different consumers, which affects the adoption processes of each consumer. These variances suggest that firms need to explore the different factors affecting consumer resistance to innovations in order to minimize the possibility of product failure. Part of the problem is that consumers may not understand the characteristics of the innovation in the same way as the manufacturers/marketers (Ellen & Bearden, 1991). It has also been suggested by researchers that smartphone manufacturers/marketers should consider the factors influencing user‟s adoption and resistance to mobile devices (Chang & Chen, 2005).
The concept of innovation resistance was presented by Sheth (1981) as the "less developed concept" in diffusion research. He researched psychology of innovation resistance and proposed two psychological constructs which seems very useful in understanding the psychology of innovation resistance. These psychological constructs are; habit/behavior towards existing products and perceived risks associated with innovation adoption. Following this model, Ram (1987) discussed innovation resistance in more details and proposed a detailed model of innovation resistance, based on this model; innovation resistance can be viewed as dependent on three sets of factors: Perceived Innovation Characteristics, Consumer Characteristics, and, Characteristics of Propagation Mechanisms. Ram‟s model was later modified by Lee and Yu (1994), with the argument that consumers' resistance is not dependent on propagation mechanism, as propagation mechanism can only influence innovation diffusion.
Numerous studies have applied factors from consumers' characteristics and innovation characteristics to assess consumer adoption of innovation, while some studies have applied these factors to assess consumers' resistance. Below is a brief discussion to mention these studies.
Different researchers have analyzed the affects of innovation characteristics on innovation adoption (He et al., 2006, Brown et al., 2003, Tan & Teo, 2000, Holak & Lehmann, 1990, He & Peter, 2007) and some on innovation resistance (Laukkanen et al., 2007, Kuisma et al., 2007). He et al. (2006) analyzed innovation characteristics factors affecting consumers' adoption, and found that compatibility and relative advantage are positively and complexity is negatively related to consumers‟ adoption of online e-payment. Laukkanen et al. (2007) researched innovation characteristics (value, risk, usage, image, etc.) as the barriers that cause innovation resistance to mobile
banking among mature consumers. Kuisma et al. (2007) used innovation characteristics and their impact on consumers to analyze the causes of consumers' resistance to internet banking.
A number of researchers have applied consumers' characteristics to examine its affects on innovation adoption (Grabner-Kräuter & Faullant, 2008, Wang et al., 2008, Tan & Teo, 2000, Karjaluoto et al., 2002), and also on innovation resistance (Cho Seong & Chang Dae, 2008). These are discussed later in the „frame of reference‟ in detail.
Several studies have been found in the literature, that examine consumers’ characteristics and its effects on consumers‟ behavior toward technological innovation, using TAM (Technology Acceptance Model; states that, the intention to use a new technological product is determined by the PU “perceived usefulness” and PEOU “perceived ease of use”) with addition to other factors like risk, self-efficacy etc. (Fang et al. 2005, Lu et al. 2003, Constantiou et al. 2006, Koivumaki et al. 2006, Han et al. 2006, Harkke 2006).
A study of physicians' adoption of a mobile system in Finland found that PU among other factors played an important role in physicians‟ intention to use the mobile system (Han et al., 2006; Harkke, 2006). Yang (2005), Yui Chi et al. (2007), and Amin (2008) applied TAM model to examine the affect of consumers’ characteristics factors on their attitude towards mobile commerce, online banking, and mobile phone credit cards respectively. Park and Chen (2007) applied TAM model with addition to “self-efficacy” to study its affect on the adoption of smartphone by medical doctors and nurses. Roberts and Pick (2004) combines the TAM model and innovation characteristics, adding the factors of reliability, security, & cost etc; to analyze the crucial factors affecting corporate adoption of mobile devices. Security risk has been found as critical factor affecting adoption and resistance behavior.
The literature review reveals that very less number of studies have investigated factors affecting consumers resistance, and even fewer studies have empirically examined consumers‟ perceptions that can make good understanding of innovation resistance (Park & Chen, 2007). Due to per se different characteristics among consumers, and the varied affect of innovations characteristics on them, both sides' perspectives and factors could cause a significant level of variation in each factor's affects on customers' resistance (He & Peter, 2007). Robert (1998) argues that, there is a need to identify and understand the factors that seem to most influence customers' resistance to innovative products. Awareness of the factors that play an important role in the adoption of innovation is crucial and a smart source of providing useful insights concerning the improvements of chances for customers' acceptance.
The study of consumers‟ resistance in the case of smartphone, based on innovation and consumers' characteristics can contribute to the innovation research field, a new breed of information/knowledge regarding consumers' behavior towards newer mobile technology. Ultimately, manufacturers/marketers would be in a better position to predict consumers' reaction/interaction with the new products to minimize/overcome the resulting consumers‟ resistance.
1.3 Research questions
a. (i) What are the causal relationships among innovation characteristic factors and consumers' resistance to smartphones?
(ii) What are the causal relationships among consumers' characteristic factors and consumers' resistance to smartphones?
b. Which factors of consumers' and innovation characteristics mainly affect/determine consumers' resistance to Smart phones?
c. What is the inter-relationship among the innovation & consumers characteristics factors?
1.4 Purpose
The purpose of this study is to identify and analyze the relationship between consumers' resistance and different factors from innovation and consumers' characteristics. Thereafter, important factors are identified which mainly affect/determine consumers' resistance to smartphones. Moreover, the inter-relationship (correlation) among the selected factors is found out, to know the affect of each factor on other factors.
a. Hypothesis
To answer the first „research question‟, a theoretical model of consumers' resistance to smartphones has been proposed and eight hypotheses (each for one factor) are constructed to find the causal relationships (test the proposed model). This theoretical model is based on some models (Ram‟s, Yu and Lee Model, & TAM) and empirical results from different studies (discussed in the frame of reference).
Based on the models and different studies on consumers behavior towards innovation, we propose that; consumers' resistance to adoption of smartphone is mostly determined by innovation characteristics; relative advantage, compatibility, perceived risk, complexity, & expectation for better products, and consumers characteristics; motivation, attitude towards existing products, & self-efficacy.
Our eight hypotheses are presented as follow:
H1= The lower the Relative Advantage, the higher the consumers' resistance to Smartphones
H2= The lower the Compatibility, the higher the consumers' resistance to Smartphones
H3= The higher the Complexity, the higher the consumers' resistance to Smartphones H4= The higher the Perceived Risk, the higher the consumers' resistance to Smartphones
H5= The higher the Expectation for Better Smartphones, the higher the consumers' resistance
H6= The lower the motivation, the higher the consumers' resistance to Smartphones
H7= The more favorable/positive consumers' Attitude towards normal mobile phones, the higher the consumers' resistance to Smartphones
b. Regression coefficients and Correlation
The second and third „research questions‟ will be answered from the results of regression coefficients and correlation respectively, and will be performed on the same empirical data collected for hypotheses testing.
1.5 Delimitations
This study is only based on those smartphones that fall under our definition for smartphone, and is limited to only young people/consumers (accessible) in Sweden. It is also limited to the opinions of consumers, as responses from mobile industry (manufacturers/marketers) have not been collected.
2
Frame of reference
In this chapter, we have discussed relevant theories and models. These theories and model are used to build a theoretical framework and propose a model to be applied on our empirical data; that will enable us to test the hypotheses and fulfill the desired purpose of the study.
2.1 Innovation
An innovation is "an idea, practice, or object that is perceived as new by an individual or other unit of adoption" (Rogers, 1995, p. 11). An innovation may composed of advancement in existing features, or establishment of new features to an existing product/service, or it might be a totally new/innovative product/service introduced in the (same or new) market (Bagozzi & Kyu-Hyun, 1999).
2.1.1 Technological Innovation
Technological „Innovation‟ is an iterative process started by the perception of a new market and/or new opportunity for an invention (technological) which directs to development/improvement, manufacturing, and then marketing tasks essential for the commercial accomplishment of the invention. This reveals two important perspectives, first, the „innovation‟ process comprises the technological development of an invention with addition to the commercial introduction of that invention to consumers, secondly, the innovation process is iterative and hence, instinctively includes the first opening of a new product and the re-opening of an enhanced and developed innovation (Garcia & Calantone, 2002). The commercialization of new product has been termed as the most critical and also crucial activity that renders its accomplishment (Gourville J. 2006). The above definition made it important to clarify and distinguish between an invention and innovation. “A discovery/invention that moves from the lab into production, and adds economic value to the firm (even if only cost savings) is considered an innovation” (Garcia & Calantone, 2002). An invention cannot get turned into an innovation unless & until it pass through the manufacturing/production and marketing activities, so and invention/discovery that does not move towards commercialization remains an invention (Connor & Colarelli, 1998).
2.1.2 Types of Innovation
Generally there are two types of innovation; incremental and radical innovation. In this study we deal with radical innovation. A radical innovation is “a product, process or service with either unprecedented performance features or familiar features that offer significant improvements in performance or cost that transform existing markets or create new ones” (Assink, 2006). It can also be defined as an “innovation that breaks with traditions in the field”. They can also be labeled as radical, discontinuous, generational or breakthrough (Dahlin & Behrens, 2005), and also disruptive innovation (Tushman & Anderson, 1986). Radical innovations are essential and eminent for manufacturers/marketers because of their capabilities to bring new means of competitive advantage, on the other hand they are necessary for consumers as they are the main source of social and economic change in everyday lives (Garcia & Calantone, 2002). The adoption of radical innovations require much more commitment and entail
higher expenditure and risks (including learning costs and psychological effort) than the adoption of incremental innovations (Heiskanen et al., 2007).
2.2 Smartphone
While looking for the definition of smartphones, we realized that there is no agreed-upon definition of Smartphone. Even, the definition of smart phone has changed over time (Jo B., 2006). The literature discusses several and somehow controversial definitions of Smartphone, however some commonalities can be found in the ways it has been defined.
Gartner, a renowned analyst house, defines "Smartphone" as "A large-screen, voice-centric handheld device designed to offer complete phone functions while simultaneously functioning as a personal digital assistant (PDA)" (Jo B., 2006).
Palm (a hand-held device manufacturer)‟ definition on Smartphone is “A portable device that combines a wireless phone, e-mail and Web access and an organizer into a single, integrated piece of hardware”, that represents radical innovation in the mobile phone industry (Mike, 2007). According to Yuan (2006), a smartphone, is any electronic handheld device that integrates the accessibility of a mobile phone, personal digital assistant, also called PDA, or other information device. Chang and Chen (2005) mentioned that smartphone devices have one common baseline characteristics: they all provide cell phone, E-Mail/Internet, and basic PDA functionality.
For this study, we define smartphones as a device that provides cell phone, E-Mail/Internet, PDA (personal digital assistant) functionality with full keyboard and relatively big screen. Considering this, we regard the following phones as Smartphones; the cell phone industry also recognizes these devices as Smartphones (CNET, 2009). - Nokia N-series (N70/N73/N78/N79/N80/N81/N82/ N85/N86/N91/N95/N96/N97)
- Nokia E-series (E50/E51/E60/E61/E62/E63/E65/E66/E70/E71/E75/E90 Communicator) - Nokia Xpress-Music Series (5700/5730/5800, etc)
- Nokia 6300/6500 Classic/ 6600/7610/7650/3250/3620/9290/9300/9500 - Samsung Omnia/Saga/Epix/BlackJack/SPH-M520/SPH-i325/SGH-i718/SCH-i760 - Samsung IP-830W/SCH-i830/SCH-i730/SP-i600/SPH-i700/SPH-i500/SPH-i300/I7500 - Apple iPhone
- HTC T-Mobile/Touch Diamond/Touch Cruise/S743/Touch Pro/Fuze - LG KS20
- AT&T 2125/ 3125/8100/8125/8525 - Palm Treo/Centro
- RIM Blackberry Storm/Curve 8900/Curve 8330/Bold/Pearl Flip 8200, etc…
2.3 Innovation Resistance
Innovation resistance is consumers' reaction towards an innovation, either because it create potential changes from a satisfactory status quo or because it is in conflict with their belief structure (Ram & Sheth 1989).
One aspect of innovation resistance is; resistance due to changes imposed by innovation (e.g. changes in consumption or product) and is called resistance to change (Gatignon & Robertson, 1989). Zaltman and Duncan (1977 p. 63) defined this as “any conduct that serves to maintain the status quo in the face of pressure to alter the status quo”. Resistance to change is a natural response of a human being to any changes that disturb the balance of living environment or firms' actions (Watson, 1971; Zaltman & Duncan, 1977). As for innovation resistance, “it is not an innovation per se that people resist but the changes associated with it” (Ellen et al., 1991; Schein, 1985). This creates the postulation of pro-change bias, which means that every innovation is excellent and everyone must implement/adopt it, because success of innovation is inevitable (Dunphy & Herbig, 1995).
Innovation resistance has been called as one of the important critical success factors for the adoption of technological innovation (Leonard, 2004), and adoption has been portrayed as the result of overcoming resistance (Szmigin & Foxall, 1998). In another research, adoption and resistance are called as the two ends of a continuum of reaction towards innovation (Lapointe et al., 2002). Ram and Sheth (1989) discovered that, the causes of innovation resistance stem from one or more of the adoption barriers. These barriers are usage, value, risk, image, and traditional barriers. The usage barrier comes when the innovation is not compatible with consumers' existing workflow, practices, or habits. The value barrier is based on the economic value of an innovation that the innovation does not offer strong performance-to-price compared to its alternative products. Risk barrier is the degree of potential risks an innovation may entail. Traditional barrier generally involve the changes an innovation may cause in daily routines, also it “a preference for existing, familiar products and behaviors over novel ones” (Arnould et al. 2004, p.722). The image barrier is associated with the innovations identity (from its origin) like the product category, brand, or the country of origin (Ram & Sheth 1989).
Different researchers have found that, even for successful new products, most of the time consumers respond in less than enthusiastic way (Gold, 1981; Brod, 1982; Murdock & Franz, 1983; Blackler & Brown, 1985; Salerno, 1985; O'Connor et al., 1990), this less enthusiasm is often termed as consumers' resistance (Ellen & Bearden, 1991). Consumers‟ resistance plays an important role in the success of innovation, as it can certainly inhibit or delay the consumer adoption, and has been termed as one of the major causes for market failure of innovations (e.g. Ram 1987, Ram & Sheth 1989, Sheth 1981)
Resistance leads consumers response towards three forms, it may take the form of direct rejection, postponement or opposition (Szmigin & Foxall, 1998, Mirella et al., 2009). Based on the studies of Mirella et al., (2009) and Szmigin & Foxall (1998), we can represent the concept of consumers' resistance in the figure as:
Innovation
Resistanc
e
Postponement
Postponement occurs when consumers delay the adoption of an innovation. It simply “refers to pushing the adoption decision to future” (Kuisma et al., 2007). Even though the innovation may be acceptable to them, but usually it is caused by situational factors, like e.g. waiting for the right time, to become capable, or to make sure the product works effectively. Postponement may take the form of acceptance or rejection after a certain time period (Szmigin & Foxall, 1998).
Opposition refers to “protesting the innovation or searching for further information after the trial” (Kuisma et al., 2007, p. 464). It is a kind of rejection, but the consumer is willing to test/check the innovation before finally rejecting it. The causes of opposition vary and can be many, e.g. habit resistance, situational factors, and consumers' cognitive style might direct them to reject innovations (Mirella et al., 2009). Most importantly, an opposition might lead the consumers to search for adequate information which can direct them to acceptance. On the other hand consumers might reject an innovation on the basis of existing awareness about the innovation when they understand that it is not suitable for them (Szmigin & Foxall, 1998).
Consumers may directly reject an innovation, which is the most extreme form of resistance (Mirella et al., 2009). When a mass of consumers reject an innovation, manufacturers usually change or iterate/modify it appropriately and then re-introduce it in the market. Rejection may occur if the innovation does not offer any valuable advantage, is complex or risky, etc (Szmigin & Foxall, 1998). Rejection can be of two types, passive and active rejection; where passive rejection occur when the innovation is never really adopted or implemented, and active rejection occur when the innovation has been considered but later rejected (Woodside Arch & Biemans Wim, 2005).
2.4 Sheth Model
Sheth (1981) researched psychology of innovation resistance and proposed two psychological constructs, which has been termed very useful in understanding the psychology of innovation resistance. These psychological constructs are; habit/behavior towards existing products and perceived risks associated with innovation adoption.
Following this model, Ram (1987) discussed innovation resistance in more details and proposed a detailed model of innovation resistance.
2.5
Ram’s Model
According to this model, innovation resistance can be viewed as dependent on three sets of factors; Perceived Innovation Characteristics, Consumers‟ Characteristics, and Characteristics of Propagation Mechanisms, where each set consists of detailed factors. Ram‟s model of innovation resistance is a useful tool for studying innovation resistance, and has been used most widely for assessing consumers‟ resistance to different innovations (Gatignon & Robertson 1991; Rogers 1995).
Source: (Ram, 1987)
In 1994 two Korean scholars, Yu and Lee modified Ram‟s model of innovation resistance and have excluded the characteristics of propagation mechanisms claiming that “propagation mechanism” is a barrier to diffusion of innovation from a social perspective rather than source of innovation resistance.
Consumers’ characteristics (Im et al. 2003) (Szmigin & Foxall, 1998) (Goldsmith & Hofacker, 1991) and Innovation characteristics (Roger, 1995, Mohr, 2001) (Tornatzky & Klein, 1982) have been termed as important in Ram‟s model of innovation resistance, affecting consumers‟ resistance (Yu & Lee 1994, Midgley & Dowling 1993) (Lassar et al., 2005, Lunsford Dale & Burnett Melissa, 1992).
In Ram‟s model of innovation resistance, the factors of innovation characteristics are; relative advantage, compatibility, perceived risk, complexity, and expectations for better products (which are raised by the problem of inhibitory effect on the adoption of other expected Innovations). On the other hand, the factors of consumers‟ characteristics are Perception, Motivation, Personality, Value orientation, Beliefs, Attitude, Previous Innovative Experience, Age, education, and income. All of these
factors have different nature of affect on different products and industries, as there is no evidence that these factors are all applicable and have the same affects on different products.
2.6 Yu and Lee Model
Yu and Lee (1994) modified Ram‟s model of innovation resistance. They distinguished innovation barriers from innovation resistance. According to Yu and Lee, innovation characteristic and consumer characteristic in Ram‟s model generate consumer resistance to innovation. However, propagation mechanism does not generate consumer resistance to innovation but plays a role as a barrier in diffusion of innovation from a social perspective. They claimed that only innovation characteristics and consumer characteristics in Ram‟s model generate innovation resistance.
Source: (Lee & Yu, 1994)
2.7 Technological Acceptance Model (TAM)
According to TAM, the intention to use a new technology is effected by the PU (perceived usefulness) and PEOU (perceived ease of use) for the specific technology. TAM has been proposed by Davis (1989) and later applied on finding consumers resistance to computer systems (Davis et al 1989). Later on, this model has been extensively applied and extended by researchers to study technology acceptance behavior and to identify the adoption decision determinants of technological innovation (Gefen et al., 2004; Hsu et al., 2004; Luarn & Lin, 2005). TAM is a subset of Ram‟s model, and specifically study technological innovation, where perceived ease of use (PEOU) is derived from complexity and perceived usefulness from relative advantage (Roberts & Pick, 2004). Later on different researchers have chosen the factor “self-efficacy” as an important tool instead of perceived ease of use (PEOU) for examining
consumers behavior to innovative technological products (Ellen & Bearden, 1991, Tan & Teo, 2000).
2.8 Related studies
Numerous studies have applied factors from consumers' characteristics and innovation characteristics to assess consumer adoption of innovation, while some studies have applied these factors to assess consumers' resistance.
Different researchers have applied Roger‟s (1983) model, to analyze the effect of innovation characteristics on consumers' adoption (He et al., 2006, Brown et al., 2003, Tan & Teo, 2000, Holak & Lehmann, 1990). The model proposed by Roger is used to examine the affect of five innovation characteristics (Relative advantage, Compatibility, Complexity, Trialability, and Observability) on innovation adoption, where most of the factors (relative advantage, compatibility, and complexity) are related to consumers‟ resistance. He et al. (2006) applied Roger‟s innovation characteristics to examine factors affecting consumers' adoption, and found that compatibility and relative advantage are positively and complexity is negatively related to consumers' adoption of Online E-payment. Im et al (2003) performed a study to identify and analyze consumers' characteristics and its affect on innovation adoption. He and Peter (2007) performed a study examining decision factors for the adoption of online payment system. These decision factors are derived from innovation characteristics, consumers' characteristics, and Technology Acceptance Model.
While examining consumers’ characteristics and its effects on consumers' behavior toward technological innovation, several studies have used TAM with addition to other factors (Fang et al. 2005, Lu et al. 2003, Constantiou et al. 2006, Koivumaki et al. 2006, Han et al. 2006, Harkke 2006). Venkatraman (1991) analyzed the relationship between personal characteristics and innovation adoption behavior and found that, it depends on consumer innovativeness type (either sensory or cognitive) and product type.
Yang (2005), Yui Chi et al. (2007), and Amin (2008) applied TAM model to examine the affect of consumers’ characteristics factors on their attitude towards mobile commerce, online banking, and mobile phone credit cards respectively. Wang et al. (2003) applied TAM model and “perceived credibility” factor, to examine consumers‟ characteristics and the subsequent affects on adoption of internet banking. Park and Chen (2007) applied TAM model with addition to “self-efficacy” to study its affect on the adoption of smartphone by medical doctors and nurses. Nysveen et al. (2005) applying TAM model investigated the moderating affects of gender in explaining intention to use mobile chat services. Roberts and Pick (2004) combines the TAM model and innovation characteristics, adding the factors of reliability, security, digital standards, cost, future web connectivity, and technology product suitability; to identify and analyze the most important factor affecting corporate adoption of mobile devices. Security risk has been found as critical factor affecting adoption and resistance behavior.
The TAM model has been criticized by Pijpers et al. (2001) and Yang (2005), as most of the studies based on this model failed to provide understanding of how consumers' perceptions of innovative technologies are formed and how these perception can be modified to increase adoption/acceptance and overcome resistance.
The literature review reveals that most of the studies have been done concentrating on consumer adoption of innovation, but very less attention have been given to their affect on consumers’ resistance (Gatignon & Robertson, 1985, 1991; Ram, 1987). The adoption and diffusion theories do not examine the process of consumer resistance to innovations. Majority of the diffusion studies have only looked at successful innovations but consumers' resistance might be present even for successful innovations (Ram, 1989). It has been argued by Ram (1989) and Sheth (1981) that it is much effective to concentrate on understanding the factors affecting innovation resistance rather than innovation adoption (Sheth 1981, Ram 1989). In the innovation diffusion process, resistance usually takes place at a stage earlier to adoption (Ram 1987, Woodside & Biemans 2005), so the first importance has to be given to identifying and understanding consumers‟ resistance. Resistance has been called as the other side of the innovation phenomenon, and it is important to study to concentrate on both sides of the coin (Kuisma et al., 2007).
In this study we have chosen relative advantage, as a substitute factor for “perceived usefulness” (PU) and complexity & self-efficacy as substitute factors for “perceived ease of use” (PEOU) in TAM. These factors have been empirically proved in the literature to have considerable effect on the two factor PU (perceived usefulness) and PEOU (perceived ease of use) of TAM (Park & Chen, 2007, Roberts & Pick, 2004) (Venkatesh & Davis, 2000). Moreover, following Ram (1989) and Yu and Lee (1994) model, the factors; compatibility, risk, expectation for better products, are chosen from consumer-dependent category of innovation characteristics. „Motivation’ and ‘attitude towards existing products’ are the factors chosen from consumers characteristics affecting innovation resistance.
2.9
Factors Affecting Consumers’ Resistance
There are two kinds of factors that affect consumers‟ resistance, and are based on consumers‟ characteristics and innovation characteristics (Ram, 1987, Kim, 2005, Yu & Lee, 1994, Dunphy & Herbig, 1995, W. Robert, 1998).
Innovation characteristics are related to the outcome and the affect of new products on consumers, which determine the amount of resistance generated (Ram, 1987) and has the power to predict consumer adoption and expected resistance. It has been found by some researchers that innovation characteristics provide greater explanation to consumers' behavior towards innovation (Agarwal & Prasad 1997). Innovation characteristics research describes the relationship between the attributes or characteristics of an innovation and the adoption or implementation of that innovation (Tornatzky & Klein, 1982).
Consumers' characteristics are the psychological characteristics of consumers e.g. how they view the innovativeness with respect to that particular product (Dunphy & Herbig, 1995). Innovation resistance is dependent on the psychological characteristics of the consumer. The important factors that have been identified as relevant to consumer behavior in innovations context are: Personality, Attitudes, Value Orientation, Previous Innovative Experience, Perception, and Motivation (Ram, 1987).
2.9.1 Innovation Characteristics Factors
According to Ram (1987), Kelly, and Kranzberg (1978), innovation characteristics can be divided into two contexts, first is consumer-independent context and second is consumers-dependent. The factors of consumer-independent context can be expected to create the same type of resistance across all consumers (Ram, 1987), and is thus out of the scope of this study. On the other hand, the affects of Consumer-dependent factors vary across different consumers. Innovation characteristics (consumer-dependent) factors effect consumers‟ decision making to adopt a new product, these factors are; relative advantage, compatibility, risk, complexity, and expectations for better products (inhibitory effect on adoption of other innovations). Understanding these factors and their affect on consumers' resistance is crucial for increasing the chances of innovation success (Ram 1987, Yu & lee 1994). Following is the detailed discussion about each factor.
2.9.1.1 Relative Advantage
The relative advantage of an innovation is the “degree to which an innovation is perceived as being better/superior than the idea it supersedes” (Rogers & Shoemaker, 1971, p. 138). This definition has also been cited by (Tornatzky & Klein 1982; Holak & Lehmann 1990). Relative advantage can be presented in economic profitability, social benefits, time saved, hazards removed (Tornatzky & Klein, 1982), and also perceived usefulness (PU) (Roberts & Pick, 2004).
Tornatzky and Klein (1982) found relative advantage to be an important factor in determining adoption of innovations, affecting consumers‟ resistance negatively. Agarwal and Prasad (1997) found relative advantage as the dominant factor that predicts consumers‟ intention to adopt or resist innovation. In general, perceived relative advantage of an innovation is positively related to its rate of adoption (Rogers 1983; Tan & Teo 2000), and negatively related to consumers' resistance (Dunphy & Herbig, 1995).
Relative advantage, in addition to its direct and negative effect on consumers' resistance, has indirect impact on perceived risk. If considerable advantages are provided with a new product/service, the expected risk maybe decreased as consumers' ignore its deficiencies/flaws (Holak & Lehmann, 1990). Moreover, relative advantage is positively related to compatibility and negatively related to complexity (Holak & Lehmann, 1990) as compatible product can be utilized effectively and may increase its relative advantage, but relative advantage may decrease if the new product is complex and consumers are unable to utilize it effectively (W. Robert, 1998).
2.9.1.2 Compatibility
Compatibility is the degree to which prospective consumers believe that the new product fits with their socio-cultural norms or is consistent with existing values, past experiences, style, behavior patterns, and needs (Dunphy & Herbig, 1995, Holak & Lehmann, 1990). It has been regarded as an important component included in attitude development (Rogers, 1995, Saaksjarvi, 2003) and is of special importance in technological markets. A general cause expressed by different consumers for resisting or not adopting new product is "no need" (Zeithaml & Gilly, 1987), even though all technological innovations usually bring about a number of benefits/advantages for
consumers (Roger, 1995). Compatibility (or simply "congruence"), in prior research has been found as a crucial factor than can predict consumers' adoption and resistance behavior (Saaksjarvi, 2003).
There are two aspects of innovation compatibility (Tornatzky & Klein, 1982): (1) it may refer to compatibility with the values or norms of the potential adopters or (2) may represent congruence with the existing practices of the adopters. The first is a kind of psychological or cognitive compatibility (e.g. compatibility with what people feel or think about a technology) while the second is a more practical/operational compatibility (e.g. compatibility with people practices).
Culture and previous experience with products can determine (to some extent) consumers' sense of security with innovation (Holak & Lehmann, 1990). It has been argued by Tornatzky and Klein (1982) that, “no matter the compatibility is normative/cognitive or practical/operational; from theoretical perspective, the innovation compatibility to the potential adopter is positively related to adoption and implementation of the innovation”. This is also supported by Dunphy and Herbig (1995) and Tan and Teo (2000), who state that compatibility is positively related to the diffusion rate and negatively related to consumers' resistance.
Compatibility may lead the innovation evaluative process due to its direct affect on purchase intention and other attributes (Holak & Lehmann, 1990). Research suggests that compatibility has a large and direct positive effect on purchase intentions; as if an innovation is perceived compatible, it is most probable that consumers will learn and get information about the innovation (Holak & Lehmann, 1990). On the other hand, the adoption rate is affected by the old/existing products, the more compatible the old/existing products are, the less consumers intentions to adopt new products (Dunphy & Herbig, 1995) and hence more consumers' resistance.
Although the impact of compatibility on other factors has not been studied empirically (Saaksjarvi, 2003), but it is expected to positively affect relative advantage and negatively affect perceived risk (Holak & Lehmann, 1990). For example, if a new product is perceived as incompatible with consumers' work/life-style, it may not be possible to recognize all its advantages. Moreover, if a new product is perceived as compatible with past experience, principles, and life-style, they will be aware of the previous items and hence much competent to judge the innovation in terms of its dominance over existing/old products. The risk (especially psychosocial risks) associated with innovation decreases, if innovation are perceived as more compatible with one's work/life-style (Holak & Lehmann, 1990).
2.9.1.3 Complexity
Complexity can be defined as “the degree to which the innovation is perceived as relatively difficult to understand, use or comprehend” (Rogers & Shoemaker, 1971, p. 154). This definition has been followed by some other researchers (Holak & Lehmann, 1990; Dunphy & Herbig, 1995).
Different researchers have found complexity as negatively related to the innovation diffusion and positively related to innovation resistance (Dunphy & Herbig, 1995,
considerable complexity demand more skills and efforts (to implement and use innovation) to increase its adoption and decrease the possibility of consumers' resistance (Cooper & Zmud 1990; Dickerson & Gentry 1983; Tan & Teo 2000).
It is generally believed that innovative products that are less complex, are easily adopted by consumers (Holak & Lehmann, 1990). There exist a negative relationship between complexity and relative advantage, as if a product is perceived as complex, it will be difficult for consumers to try it and hence cannot be utilized for its advantages (Holak & Lehmann, 1990, W. Robert, 1998). Complexity as a factor of consumers' characteristics is expected to affect consumers' intention and lead towards adoption through relative advantage, risk, and also self-efficacy. It has been argued by Holak and Lehman (1990) that greater risk is associated with innovation which is perceived as more complex, so, there is a positive relationship between complexity and perceived risk. Complexity effect consumers' adoption indirectly through perceived risk (Holak & Lehmann, 1990). 2.9.1.4 Perceived Risk
Bauer (1960), Webster (1969), and Ostlund (1974) introduced risk as an additional dimension in the diffusion and adoption of innovation, which is then added by Sheth (1981) and Ram (1987) as another factor affecting consumers resistance. Here we are talking about the degree of perceived risk associated with adopting & using innovation. It is believed as positively related to consumers resistance and negatively related to adoption (Ram, 1989, Dunphy & Herbig, 1995).
Newer technologies/products may be perceived by consumers as more risky. Research has shown that the perceived risk is a critical determinant of a consumer‟s willingness to adopt an innovation (Shimp & Bearden, 1982). As it is very difficult to capture risk as an objective reality (Dowling & Staelin, 1994), it is interpreted as the “consumer‟s subjective expectation of suffering a loss in pursuit of a desired outcome” (Yiu Chi et al., 2007, p.336). Consideration for the consequences of an action, including the perceived risk, are critical aspects that formulate attitude towards that action (Crisp, Jarvenpaa, & Todd, 1997), thus perceived risk may enhance consumers' resistance from adoption of an innovative product. Therefore perceived risk is believed to have positive relationship with consumers‟ resistance (Yiu Chi et al., 2007).
Even in situations, where a consumer has evaluated and considered to adopt an innovation, perceived risk and uncertainty create substantial barriers to adoption (Aggarwal et al., 1998). Innovation always involves some degree of perceived risks because of uncertainty (Ram & Sheth, 1989), so innovation that associated with considerable perceived risk, has slower rate of diffusion (Dunphy & Herbig, 1995) and higher consumers‟ resistance (Ram, 1989).
Usually, perceived risk is termed as an innovation characteristics, however Fain and Roberts (1997) argue that most of the time, risk is rather a perception of a consumer than merely a characteristic of an innovation. But looking at the dominant literature, we have included perceived risk in innovation characteristics.
Researchers have identified six key dimensions of perceived risk, which are; financial, performance, physical, time, social, and psychological risks (e.g. Cherry & Fraedrich, 2002; Ram, 1989; Dholakia, 2001).
Following are the definitions of the perceived risk dimensions (Jacoby & Kaplan 1972; Hirunyawipada & Paswan 2006; Dholakia, 2001; Ram, 1989; Kuisma, Laukkanen & Hiltunen 2007).
a) Financial risk captures the financially negative outcomes for consumers after they adopt new products. It is also called the fear of economic loss.
b) Performance risk concerns with the belief that the new product / innovation will not perform as anticipated. It is also called the fear of performance uncertainty.
c) Physical risk is the perception that products will be physically harmful to adopters.
d) Time risk relates to the perception that the adoption and the use of the product will take too much time (see e.g. Roselius 1971).
e) Social risk has to do with the negative responses from consumer‟s social network. Ram called it “the fear of social ostracism or ridicule”.
f) Psychological risk is the fear of psychological discomfort. The nervousness arising from the anticipated post-purchase emotions such as frustration, disappointments, worry, and regret.
From these six kinds of risk, financial, performance and also security risk (security of important personal information and the product itself) have been found as the most important types of risk related to smartphone adoption (Richardson, 2003; Roberts & Pick, 2004).
2.9.1.5 Expectation for better products
According to Ram‟s model of innovation resistance, „inhibitory effect on the adoption of other innovations‟ is one of factors that affect innovation resistance. In some cases, the adoption of one innovation products may have an inhibitory effect on the adoption of other innovations (Ram, 1987). If a person purchases an innovative product such as expensive high resolution digital cameras, the person is not likely to purchase another new digital camera with improved performance and more features within a short period of time. The person is postponing his/her purchases. He/she reasons, quite correctly, that if he/she waits, a better product with a lower price tag will soon be in the market (Ram & Sheth1989). Products based on new technologies are especially susceptible to this factor. For example, even in the corporate world, many companies decide to wait for a new generation of products with a better performance-to-price ratio before upgrading their computer systems (Ram & Sheth 1989).
Lee and Yu (1994) and Kim (2005) used a term of „expectation for better products‟ rather than „inhibitory effect on the adoption of other innovations‟ as it is easy to understand and give a clear meaning. In this study, we will also use the term „expectation for better products‟.
2.9.2 Consumers Characteristics Factors
For this study we have chosen „Motivation‟ and „attitude towards existing products‟, as motivation is believed as the central key factor driving consumer behavior (Barczak et al., 1997), and “attitude towards existing product” is to examine the role of existing
products in driving consumers resistance. Moreover self-efficacy has been added, as it believed to play a major role in technological innovative products (Ellen et al. 1991, Compeau & Higgins 1995). One of purposes behind choosing these factors is because of their easy measurement procedure and intensive use by different researchers (Lee Matthew et al., 2007, Barczak et al., 1997, Wang et al., 2008, Wang et al., 2003).
2.9.2.1 Motivation
Motivation is defined as “goal-directed arousal” that drives consumers need (MacInnis & Moorman, 1991). It entails internal processes that provide behavior with power and direction. Power describe the strength, determination, and concentration of the concerned behavior, while direction provides a specific purpose to the behavior (Lee Matthew et al., 2007).
Herzberg at el. (1959) theorized that behavior can be motivated extrinsically and intrinsically. Based on this, motivation is divided in two types, the extrinsic motivation and intrinsic motivation which are two kinds of drivers that evoke a specific outcome behavior. Perceived usefulness and perceived enjoyment are typical examples of extrinsic and intrinsic motivation respectively, in technology adoption context (Lee Matthew et al., 2007).
Extrinsic motivation involved performing an activity for achieving other goals i.e. to gain other valued outcomes rather than the activity itself (Davis, Bagozzi, & Warshaw, 1992) e.g. a decision to use computer for writing a letter (Lee Matthew et al., 2007) where behavior is driven by its perceived value and expected benefits. Davis et al. (1992) argue that perceived usefulness (PU) and perceived ease of use (PEOU) are the two paradigm of extrinsic motivation, and found that if users perceive something (technology) to be useful and easy to use, it is more probable that they will use it. It has also been found by Devis et al. (1992) that consumers' perceived usefulness increases through increase in perceived ease of use. In Technology Acceptance Model (TAM) the two factors, PU and PEOU are widely applied in research studies on technology acceptance (Lee Matthew et al., 2007).
Intrinsic motivation involves performing an action for its own sake, as the action is itself exciting, engaging, entertaining etc. It means the passion to do an activity for the reward which derives from the enjoyment of the activity itself e.g., expressing personality and status by using a product. Looking at the perspective of intrinsic motivation, behavior is provoked from the feelings of pleasure, joy, and fun (Lee Matthew et al., 2007). It has been empirically proved that both extrinsic (e.g., perceived usefulness and perceived ease of use) and intrinsic (e.g., perceived enjoyment) motivators are important to the formation of intention to use (adoption) (Lee Matthew et al., 2007).
2.9.2.2 Attitude towards existing products
This is a general factor, which examines consumers' attitude toward existing products and is influenced by tradition and the abilities of existing product in serving consumers needs and wants. The tradition value is associated with individual‟s favorable attitude towards the past and present, and shows individual‟s respect for culture, social norms, and traditions (Schwartz, 1992). The tradition value implies consumer‟s favorable
attitude toward the products that they are currently using. In such case, consumers will be unwilling to replace their old and still functional products with innovative products. In this era, products life cycle is becoming shorter and shorter and competition getting tougher, new products are coming in the market with much faster pace, and existing products/technologies often become outdated very quickly and prematurely. Due to which, plenty of opportunities are available to consumers to abandon their existing products, and switch to much advanced/improved new products. But, consumers with strong favorable attitude toward existing products will resist innovative products and will continue using their existing products until they fail to function (Wang et al., 2008). It has also been found by researchers that consumers who are not satisfied with the existing products are more likely to adopt change and go for new products, on the other hand, consumers who are satisfied with the existing products will keep up using the same (Karjaluoto et al., 2002).
2.9.2.3 Self-Efficacy
Compeau and Higgins (1995, p. 193) define self-efficacy as “an individual‟s perception of his or her ability to use a technological innovative product”. Self-efficacy is a determinant of perceived ease-of-use and the usability of an item (Davis 1996). It is also defined as, “an individual‟s self-confidence in his or her ability to perform a behavior” (Bandura 1977, 1982). Self-efficacy refers to the confidence in one‟s ability and competence to manage and perform the courses of action required to accomplish a desired outcome (Bandura 1997a, 1982), and initiates from different origins including performance achievement, previous experience, personal interests, etc. (Bandura, 1997b).
It has been found by some researchers that self-efficacy has the power to foresee intentions to use a variety of technological innovation (Hill et al., 1986). A consumer with low self-efficacy will probably select a product which can be handled easily, even if there are better/advance products available. Ellen et al. (1991) empirically verified that self-efficacy is also a factor that affects resistance to technological innovations. Other researchers have also considered consumers' self efficacy as a very important factor to study resistance and diffusion of innovation (Tan & Teo, 2000).
Self-efficacy is selected as one of the factors in this study as prior studies have revealed that self-efficacy shows a substantial affect of “consumers' perceptions of his ability to use the new technological product” on his decision for product adoption (Park & Chen, 2007). It has been argued that, without skill, performance is not achievable; without self-efficacy, performance may not be endeavored (Compeau & Higgins, 1995). Consumers' self-efficacy and their perception bring about a causal relationship between the adoption of technological innovation and consumers' cognitive factors. Across a wide range of behaviors, self-efficacy has been shown to influence the willingness to act as well as actual action initiation (Bagozzi & Kyu-Hyun, 1999).
2.10 Hypotheses formulation
To identify the causal relationship among consumers' resistance to smartphones and the above discussed eight factors, we set up hypotheses based on the models of innovation
