<p> </p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p> </p> / The Presentation (Defense) has been attended by Cecilia Bjursel instead of our supervisor Desalegn Abraha.

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 &amp; 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 &amp; 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 &amp; Motivation are found as positively correlated, and Perceived Risk &amp; 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. / The Presentation (Defense) has been attended by Cecilia Bjursel instead of our supervisor Desalegn Abraha.

Submitted by Cristiano Amaral (cristiano.ab.castro@gmail.com) on 2018-03-22T19:12:42Z No. of bitstreams: 1 TESE CRISTIANO AMARAL Final.pdf: 3722353 bytes, checksum: 7441768901a6b92171fb0a12d72bc78c (MD5) / Approved for entry into archive by Debora Nunes Ferreira (debora.nunes@fgv.br) on 2018-03-23T16:49:21Z (GMT) No. of bitstreams: 1 TESE CRISTIANO AMARAL Final.pdf: 3722353 bytes, checksum: 7441768901a6b92171fb0a12d72bc78c (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-03-23T17:43:03Z (GMT) No. of bitstreams: 1 TESE CRISTIANO AMARAL Final.pdf: 3722353 bytes, checksum: 7441768901a6b92171fb0a12d72bc78c (MD5) / Made available in DSpace on 2018-03-23T17:43:03Z (GMT). No. of bitstreams: 1 TESE CRISTIANO AMARAL Final.pdf: 3722353 bytes, checksum: 7441768901a6b92171fb0a12d72bc78c (MD5) Previous issue date: 2018-02-28 / The Diffusion of Innovation literature is based on well succeeded products and present a pro-change bias, an assumption that innovations are positive and will be adopted by all consumers. However, a large portion of new products fail and those that do not fail are not promptly adopted, indicating that consumers’ natural response to innovations is resistance. Despite its relevance to both researchers and managers, little research has been conducted toward a deeper understanding of consumers’ resistance to innovation. The extant literature presents two types of resistance, Passive Innovation Resistance (PIR) which is the antecedent of Active Innovation Resistance (AIR). Notwithstanding the indications that affect plays a major role in consumers’ decision-making process, mostly cognitive factors are listed as antecedents of both. Thus, the studies herein aim to evaluate the impact of affect on consumers’ resistance to innovation. In a series of studies based on structural modelling, it is presented here indications of the moderating role of affective state in the relationship between PIR and AIR, as well as of the existence of both cognitive and affective active resistance to innovation. Considering both forms of AIR yields higher explanatory and predictive power as to intention to adopt that considering only the cognitive form of AIR, which is the prevailing understanding of AIR in the literature. Also, the affective form of AIR alone is shown to provide better results that the cognitive form alone. / A literatura de Difusão da Inovação baseia-se em inovações bem-sucedidas e apresenta um viés pró- mudança, uma premissa de que inovações são positivas e serão adotadas por todos os consumidores. No entanto, uma grande parcela de novos produtos falha ao ser lançada no mercado e aqueles que não falham não são prontamente adotados por todos os consumidores, o que indica que a resposta natural do consumidor às inovações é a resistência. Apesar de sua importância tanto para pesquisadores quanto para gerentes, poucas pesquisas foram realizadas objetivando um melhor entendimento da resistência do consumidor à inovação. A literatura existente apresenta dois tipos de resistência: a Resistência Passiva à Inovação que é antecedente à Resistência Ativa à Inovação. Desconsiderando as indicações de que afeto possui um papel importante no processo decisório do consumidor, os fatores apresentados como antecedentes dos dois tipos de resistência são predominantemente cognitivos. Assim, a pesquisa aqui apresentada possui o objetivo de avaliar o impacto do afeto na resistência do consumidor à inovação. Em uma série de estudos baseados em equações estruturais, são apresentadas indicações de que afeto possui um papel moderador na relação entre Resistência Passiva e Resistência Ativa à Inovação, assim como acerca da existência de Resistência Ativa Cognitiva e Resistência Ativa Afetiva à Inovação. Considerando-se ambas formas de resistência ativa, obtêm-se maior poder explicativo e preditivo quanto à intenção de adotar a inovação por parte do consumidor do que se obtém utilizando-se apenas a forma cognitiva de resistência ativa, que é a dominante na literatura. Além disso, é demonstrado que a Resistência Ativa Afetiva por si só apresenta melhores resultados que a Resistência Ativa Cognitiva.