An empirical comparison between China and USA market on smartphone adoption

Kim, Jong Seok Article An empirical comparison between China and USA market on smartphone adoption Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Kim, Jong Seok (2022) : An empirical comparison between China and USA market on smartphone adoption, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 9, Iss. 1, pp. 1-18, https://doi.org/10.1080/23311975.2022.2036309 This Version is available at: https://hdl.handle.net/10419/288391 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Cogent Business & Management ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oabm20 An empirical comparison between China and USA market on smartphone adoption Jong Seok Kim To cite this article: Jong Seok Kim (2022) An empirical comparison between China and USA market on smartphone adoption, Cogent Business & Management, 9:1, 2036309, DOI: 10.1080/23311975.2022.2036309 To link to this article: https://doi.org/10.1080/23311975.2022.2036309 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 06 Mar 2022. Submit your article to this journal Article views: 1687 View related articles View Crossmark data Citing articles: 1 View citing articles OPERATIONS, INFORMATION & TECHNOLOGY | RESEARCH ARTICLE An empirical comparison between China and USA market on smartphone adoption Jong Seok Kim 1 * Abstract: This study examines the factors affecting users’ adoption of the smartphone as an innovative device. Prior studies on the acceptance of smartphones have primarily focused on the impact of the technological benefits and characteristics. In this study, the author propose an integrated model of smartphone adoption that incorporates product benefit, technological capabilities, perceived product innovativeness, attitude for product, consumption pattern, word of mouth and advertising into the technology acceptance model in China and USA. The author used a structural equation model (SEM) which was empirically evaluated by using survey data collected from 3000 respondents with demographics to explore their perception and attitudes toward smartphone adoption intention. The results show that product benefit, technological capabilities, consumption pattern change and WOM have all positive effect on the perception of innovativeness. Attitude toward a product turned out to play mediating role between perceived innovativeness and intention of adoption. That is very valuable implication for manufacturers to prepare a marketing and to win bigger market share. Subjects: Information & Communication Technology; ICT; Statistics for Social Sciences; Management of Technology & Innovation; InformationTechnology Industries Keywords: SEM; PLS; innovation; smartphone adoption; technology acceptance mode 1. Introduction Mobile technologies have penetrated consumer markets throughout the world. It is likely to make a deep influence on business activities, consumer behavior, and national and global markets. The smartphone industry has been also steadily developing and growing, both, in terms of market size and models. Globally, by 2021, 40% of the world’s population is predicted to own a smartphone. The ABOUT THE AUTHOR Dr Kim is currently a Professor at Sungkyunkwan University, Suwon, Korea. His research interests include choice modeling, new product development, technology forecasting, new product adoption, big data/cloud/IoT, manufacturing & service operation management, business model innovation, future mobile market forecasting, and high-tech marketing. He received an M.S. in ORST (Operation Research & Applied Statistics), M.B.A. in Business Administration from RPI, Troy, NY and Ph.D. in Management of Technology from Yonsei University, Seoul, Korea. He is also listed in The Marquise Who’s Who in the world in PUBLIC INTEREST STATEMENT This article is to help mobile manufacturers’ marketing strategies and product strategies in the US and Chinese markets. This is a paper to identify key factors that influence consumers when purchasing new mobile devices. Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 1 of 18 Received: 05 November 2021 Accepted: 24 January 2022 *Corresponding author: Jong Seok Kim, Responsible for Academic-Industrial Cooperation Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon-Si, Gyeonggi-do 16419, Republic of Korea E-mail: [email protected] Reviewing editor: Albert W. K. Tan, Asia Pacific Graduate Institute, Shanghai Jiao Tong University, Singapore Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. global smartphone market is shifting. Over the next few years, the vast majority of growth is expected to come from developing countries as the average selling price of devices continues to fall. Meanwhile, core markets, such as the United States and China, are beginning to mature. Nearly five out of six mobile users in the US now have a smartphone, and almost all of those users are on iOS and Android phones. Meanwhile, the Chinese market continues to saturate as the share of firsttime buyers increasingly shrinks. The increasing demand for high-speed data connectivity for integrated IoT (Internet of Things) applications, such as energy management and smart home products, is anticipated to propel the adoption of 5 G smartphones. The smartphone market is a highly competitive market, dominated by established players such as Samsung, Huawei, Apple, and Xiaomi, among others. Most of these players keep launching new models with small technology changes such as battery power, camera configuration, and/or processor (Mordor Intelligence, 2021). Given that the smartphone market has evolved rapidly, the author aims to analyze the influence of various factors in customers’ decisions to buy smartphone by Rahim et al. (2016). Technology Acceptance Model (TAM) was used in this research to identify and compare the factors affecting the adoption of smartphones by smartphone users. For the purpose of analysis, the primary attributes for applying TAM were derived from a survey of 3000 mobile users, after which 13 hypotheses were established in the model and verified using the structure equation model (SEM) for two countries. The goal of this investigation was to identify the key determinants that affect the adoption of smartphones in the USA and Chinese market. The author established the key determinants that affect adoption of smartphone by examining research in the field of technology acceptance, including by (Chung and Chun, 2011; Davis, 1986; Davis et al., 1989; S. Kim and Garrison, 2009; Putzer and Park, 2012; Van Biljon and Kotzé, 2007; Venkatesh et al., 2003) and by conducting survey research. With these backgrounds, the research in this paper has several purposes. First, this study is to investigate the acceptance and adoption of smartphones and factors affecting such acceptance behaviors, and to explore the critical external variables affecting users’ attitude to use and perceived usefulness of smartphone. Second, the author intends to take a consumer’s perspective in evaluating the product innovativeness by survey research. Third, this research is to understand the relationship between the perception of new product innovativeness and the consumer’s intention of adoption. Lastly, the author tries to obtain different characteristics of each country. The analysis results show that product benefit, technological capabilities, consumption pattern change and WOM have all positive effect on the perception of innovativeness. Attitude toward a product turned out to play mediating role between perceived innovativeness and intention of adoption by two countries, though the degree of influence is not equal. The author expects the research findings to provide valuable information for understanding how the mobile market has evolved and what values the customers wants as the market evolves. The study also draws a number of practical insights and provides vendors seeking to enter the Chinese and USA marketplace with specific information about mobile users’ perceptions, intentions and adoption. There is no guarantee that technology advancements will translate into successful innovation adoption. Consumers’ acceptance and intentions to adopt the new technology by Son et al. (2011) are important aspects of new product marketing. Also, this study confirmed and compared earlier results from previous researchers and proposed a solid model that can be used for further studies. The paper consists as follows: The theoretical and methodological background of this research is explained in the literature review. Then, the author describes the overall research process and Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 2 of 18 establish hypotheses. Next, the author summarizes and discuss the empirical analysis results of SEM, and conclude with notes about contributions and future research directions. 2. Theoretical backgrounds 2.1. Technology acceptance model (TAM) Technology Acceptance Model (TAM) was proposed for explaining and predicting consumer acceptance of an information system, and it is designed specifically to interpret the acceptance process of information technologies (F. D. Davis, 1989). The original TAM consists of five components, which include perceived usefulness (PU), perceived ease of use (PEOU), attitude toward using, behavioral intention (BI) to use, and actual system use (Wu & Wang, 2005). TAM assumes that whether to adopt a particular technology is determined by two key factors: PU and PEOU for the technology (see, Figure 1). Here, PU is defined as the degree to which a person believes that using a particular technology would enhance his or her job performance, while PEOU is defined as the degree to which a person believes that using a particular technology would be free of effort (Davis, 1989). On the other hand, BI is defined as future behavior of individuals, which entails subjective probability as it relates to actual behavior (Engel & Blackwell, 1982). Actual behavior is the determinant factor in taking specific action, and BI relates to actual behavior (Ajzen & Fishbein, 1980). Subsequent research concludes that the attitude variable has weak predictors of BI (Taylor & Todd, 1995). The current study focuses on diffusion in the smartphone industry (also known as the converged mobile device market)—in particular, the adoption of Apple iPhone. The Apple iPhone is a cultural icon of the digital age (Morrissey & Brian, 2009). Mobile phones are one of the most conspicuous examples of such innovations achieving a large penetration rate in many markets (John, 2012). 2.2. Product acceptance The author is to examine the acceptance of a product evaluated as innovative on the consumer’s perspective and the product performance on the company’s perspective through these standards. Examining whether positive linear relationship is shown between consumer’s perception on innovativeness and product acceptance attitude is meaningful for generally, by perspective of the company, results show of a positive linear relationship (Henard & Szymanski, 2001) between the innovative product and its performance. This is because the performance (success or failure) of a product (evaluated as innovative and put out in the market), in strict sense, will eventually be evaluated in the process for its innovativeness and acceptance by the customers (Hoffman et al., 2005; Huh & Kim, 2008; Kotler et al., 2003; Olshavsky & Spreng, 1996; Winer, 2007). Consequently, in this study, the author will examine what factors influence consumers to perceive the innovativeness of a product released by a company and what influence this perception of innovativeness has on the attitude and intention to acceptance. This concept later found applications in studying the adoption of consumer products such as mobile phones (Guseo & Guidolin, 2010). In this paper, the study has been made to understand the importance of user Figure 1. Technology acceptance model (Davis, 1989). Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 3 of 18 behavior and acceptance in determining one’s behavior to use indigenous technology. The conceptual model combined our proposed research which consists of product benefit, technology capability, consumption pattern change (Veryzer, 1998b), and word-of-mouth (Seema Pai, 2007). The research in this paper has several objectives. First, this study is to explore cross-cultural differences on mobile technology in global business environments (Sanakulov & Karjaluoto, 2017a) and to compare with different countries. Second, the author intends to take a consumer’s perspective in evaluating the product innovativeness and its performance. Third, this research is to understand the relationship between the perception of new product innovativeness and the consumer’s intention of adoption. The goal here is to provide insight into key factors that affect the evaluation of new products rather than to provide a comprehensive list of correlated with product success. 2.3. Product innovativeness Product innovativeness refers to the degree of familiarity organizations or users have with a new product (Balachandra & Friar, 1997). The degree of innovativeness can be measured by the difference between the newness of the product and the existing technology or practices in the organization. High levels of product innovativeness are fundamentally new to consumers and the current market. Also, they are associated with revolutionary changes in technological resources within the organization (Dewar & Dutton, 1986). The activities in developing high innovative products require more knowledge resources and additional skilled technicians, autonomy and dynamic capability of the team (Darawong, 2018). New Product Development team in companies seek always breakthroughs in high innovativeness so therefore they encounter high levels of operational and market uncertainty (O’Connor & Veryzer, 2001). Essentially, high innovative products can enhance or adopt customer responsiveness (Ganji et al., 2018) and willingness to buy in highly competitive markets (Lee & Johnson, 2017). On the other hand, low product innovativeness involves minor changes or simple improvements of the product related to current business operations and products. It includes developing products that are similar to competitors or changing product attributes through the use of existing technology, knowledge and skills (Tushman & Anderson, 1986). In addition, product innovation may be viewed a lying along dimension reflecting in: product benefits (Richard T et al.), technology capabilities (Abdul et al.), consumption pattern or usage patterns (Joe et al.), and marketing mix variables (Gatignon & Robertson, 1991). The product benefit refers to the new capabilities of the product in terms of the needs that it satisfies as perceived and experienced by the customer or user. Technological capability refers to the degree to which the product involves expanding technological capabilities (i.e., the way functions are performed) beyond existing boundaries. The consumption pattern refers to the degree of change required in the thinking and behavior of the consumer in using the product. 2.4. New product characteristics association with product adoption The author examines how new product characteristics affect the rate of diffusion, three product variables have been identified and have gained widespread acceptance: relative advantage, absolute excellence in technology, the changes in usage behavior. Relative advantage is the perceived desirability or the benefit derived from a new product relative to those benefits offered by other existing products. Relative advantage refers to the degree to which an innovation is perceived as an advantage over an established solution. The greater the relative advantage, the faster the adoption of a new innovation. Nam and Kim (2004) found that new products with a relative advantage can achieve the greatest market penetration. Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 4 of 18 For the new product to gain rapid acceptance, the product must be seen as being more attractive than other alternatives. The product’s attributes that are being used for differentiation purposes must also be perceived as both excellent and significant (Onkvisit & John, 1989). The relationship between perceived advantage and innovativeness is positive: the greater the perceived advantage, the more likely it is that the product will be adopted. In other words, both relative advantages over existing products and absolute advantage in technology factors have positive influence on consumers’ perceptions on product innovativeness, acceptance intentions, and acceptance processes. However, change factors of usage behavior have positive effects on the level of perceived innovativeness but have a negative effect on product acceptance processes. An examination of previous research has shown that change factors of usage behavior have negative effects on attitude toward a new product. The author found different results that change factors of usage behavior have positive effects on attitude toward a new product. Therefore, the author has additionally added the positive process between the change factors of required usage behaviors with product acceptance besides the assumption of its positive effects on perceptive innovativeness. Also, this study considers the intermediary role of product attitude in relationship with innovativeness perception and has established a hypothesis of direct positive process between change factors of usage behavior and attitude towards product. Tsiros and Mittal (2000) and Buttle (1998) found that the valence of consumption experience determines the extent to which people talk about their product or service encounter. Veryzer (1998b) defined product innovativeness as a form of continuum within continuous and discontinuous factors and explained the origin of innovativeness as 1) relative advantage over existing products 2) high technology and 3) required changes in usage behaviors where discontinuous changes are perceived in one or more of the above origins in cases of discontinuous innovativeness. Research by Huh and Kim (2008) on mobile phone purchases in Korea found that if a product upgrade has innovative features, it will influence consumer adoption of a next-generation product. Samsung & Apple have continued to strive to include product improvements with innovative features, such as the introduction of the Face ID, MagSafe wireless charging, longer battery life, better camera, 3x optical zoom, great performance, 5 G, and best video quality as part of an iPhone 13 upgrade (The Guardian, 2021). Research by Songpol et al. (2009) showed that technological innovations will be adopted more quickly due to the moderating effect of public/private consumption. This notion of social influence of users showing off or publicly consuming their product is highly applicable to the successful adoption of the Apple iPhone. In conclusion, all the above details will be put together to establish a hypothesis on the determinants which consumers perceive the level of innovativeness of new products. This suggests the following hypothesis: H1-1: The relative advantages of new products over existing products will have positive effects on the perception of product innovativeness. H1-2: Absolute excellence in technology of new products will have positive effects on the perception of product innovativeness. H1-3: The changes in usage behavior required for new products will have positive effects on the perception of product innovativeness. More details of measurement variables about this model are displayed in Table 2. Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 5 of 18 Next, the author tests the proposed model based on empirical data. 2.5. Direct formation attitude for new product The relationship between innovativeness and product acceptance has formerly been evaluated as linear and nonlinear in previous studies. Therefore, in establishing the hypothesis for product innovativeness and consumer attitude & intention of product acceptance, this research will establish its own hypothesis based on the positive (+) linear relationship (Henard & Szymanski, 2001) between innovativeness and product acceptance indicated in previous researches as well as verifying the nonlinear relationship (Goldenberg et al., 2001; Steenkamp et al., 2003; Veryzer, 1998b) between the two factors. However, the author hopes to change the use of the term “product performance” to “product acceptance” in the future as the product performance in consumers’ perspective stands for product acceptance. Meanwhile, Rogers (1995) stated that both favorable and unfavorable attitude formation has certain effects on product acceptance. However, this study will assume that the level of product acceptance is higher for consumer groups with better attitude towards products and focus on looking into the intermediary roles of consumer attitude in the relationship with “perceived innovativeness—attitude formation—intentions for product acceptance”. This will eventually be another approach to verifying the product acceptance hierarchy model, which Gatigon and Robertson (1985) evaluated more appropriately for high involvement products. This suggests the following hypothesis: H2: The level of perceived innovativeness for new products will have positive effects on attitudes towards products. H3: Attitude towards new products will have positive effects on product acceptance intentions. H4: Attitude towards new products will mediate the perceived effect levels. 2.6. The role of WOM in the new product adoption Communication strategy is a critical element of new product adoption. The decision to adopt a new product is determined by the success of a sequence of two stages: product awareness and product adoption. Previous research has established the importance of WOM as a driver of new product diffusion (López et al., 2013). Studies have also highlighted the importance of WOM in new product diffusion. However, the expansion of new media has facilitated the development and management of word-of-mouth campaigns. Leonard-Barton (1985) shows a positive relationship between opinion leaders’ attitudes and product adoption rates, suggesting that experts can influence consumers, regardless of whether the WOM is positive or negative. Sweeney et al. (2014) shows that positive WOM was more effective and positive WOM messages had a greater effect on people’s willingness to use a service than did negative WOM. In many cases, the adoption of innovativeness consumer technology products is driven in part by the desire to gain approval and avoid disapproval (Slama & Wolfe, 1999). WOM is perceived as being more helpful, because consumers can find richer and more varied information than advertising can provide, by mixing objective and subjective information (Ghose & Ipeirotis, 2007). Thus, it is more likely that individuals will talk about a new product when receiving information from other consumers, than when exposed to advertising. For this reason, word of mouth communication should have a relatively greater impact on adoption decisions in product categories where (1) consumers attach meaning to innovation adoption; and (2) innovation use is observed by others. This suggests the following hypothesis: Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 6 of 18 H1-4: Word of mouth message new product will have positive effects on perception of product innovativeness. Advertising for new product can be reasonable means for consumer’s attitude (Wansink et al., 1998). That is true for the relationship between the construct attitude toward advertising and attitude toward mobile marketing (Tsang et al., 2004). Many empirical studies of attitudes toward mobile advertising (Okazaki, 2004; Tsang et al., 2004) borrowed the factors from internet advertising to predict consumer. Previous studies have shown advertising to be the tool that works best during the first stage of introduction. Advertising allows firms to make consumers aware that it has developed an innovation over existing state-of-art product. Consumers become generally aware of a new product only gradually. In this gradual process of product knowledge diffusion, the more a product is advertised, the more consumers become aware of the product. Lavidge and Steiner (1961) first suggested that consumers respond in terms of a hierarchy of effects, which is a sequence of stages a prospective buyer goes through from initial awareness of a product to eventual action. The sequence is Awareness→Interest→Evaluation→Trial→Adoption. Later, Hansen (1972) suggests the AIDA model which consumers move from an Awareness→Interest→Desire→Action. H1-5: Advertising for new product will have positive effects on perception of product innovativeness. It is a widely accepted notion that word-of-mouth communication plays an important role in shaping individual’s attitude and behaviors (Brown & Reingen, 1987). Repeatedly, research has shown the importance of consumer word-of-mouth in the formation of attitudes (Bone, 1995). The effects of WOM on the receiver’s attitudes have been studied. H6: Word of mouth message about new product will have positive effects on attitude toward new product perception. Consumers frequently rely on word-of-mouth when considering the purchasing of a new product or service (Brown & Reingen, 1987). The impact of word-of-mouth on purchase decisions (Seema Pai, 2007) was also supported in this paper. Recently, several researches have started to examine the impact of specific elements of word-of-mouth on consumer choice and purchase decision (Dellarocas et al., 2004; Godes & Mayzlin, 2004b). Consumer attitudes toward advertising in general have long been found to be negative. Zanot (2015), for instance, found that attitudes toward advertising became increasingly negative after the 1970s. Early surveys of consumer attitude revealed somewhat positive results. Gallup found that a majority of respondents liked advertising and found it to be informative. More people held favorable attitudes toward advertising than unfavorable attitudes (Bauer & Greyser, 1968). H7: Word of mouth message about new product will have positive effects on product acceptance. H8: Advertising for new product will have positive effects on attitude toward new product perception. Advertising is statistically significant on intention to adoption of mobile phone (Adams et al., 1992; Montoya-Weiss et al., 1994; Y. S. Sohn & Ahn, 1997). Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 7 of 18 Table 6. 95% bootstrap confidence interval for path coefficient of SEM Latent variable Path Coefficient China USA All Less than 20 More than 40 All Less than 20 More than 40 Product Benefit → Perceived Product Innovativeness 0.184** 0.217** 0.159** 0.206** 0.128 0.210** Technology capability → Perceived product innovativeness 0.497** 0.481** 0.528** 0.187** 0.107 0.159* Consumption pattern change → Perceived product innovativeness 0.165** 0.179** 0.154** 0.354** 0.416** 0.361** Attitude for product 0.286** 0.273** 0.303** 0.146** 0.114* 0.165* Perceived product innovativeness → Attitude for product 0.452** 0.446** 0.463** 0.566** 0.536** 0.636** Attitude for product → Intention of adoption 0.231** 0.250** 0.178** 0.374** 0.374** 0.233** Word-Mouth → Perceived product innovativeness 0.026 0.018 −0.034 0.169** 0.300** 0.131** → Attitude for product 0.206** 0.220** 0.214** 0.670** 0.100 −0.011 → Intention of adoption 0.571** 0.563** 0.629** 0.364** 0.331** 0.429** Advertisement → Perceived product innovativeness 0.039** 0.025 0.104** 0.115** 0.081* 0.171** → Attitude for product 0.072** 0.072** 0.040 0.158** 0.173** 0.181** → Intention of adoption 0.077** 0.071** 0.116** 0.216** 0.259** 0.343** ** Significant parameters at 5% bootstrap confidence interval; * Significant parameters at 10% bootstrap confidence interval Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 14 of 18 Also, by modeling Veryzer (1998b)’s innovation evaluation criteria into leading variable, it was verified that while relative advantage, technology capability compared to the original product influence innovation perception in a similar size, the impact size of the required consumption pattern change is significantly smaller than the two variables. Moreover, contrary to Veryzer (1998b)’s result that the required consumption pattern change gives a positive (+) impact to consumers’ perception on product innovation but gives a negative (-) impact on product attitude, this research obtained a result that it gives a positive (+) impact. Second, the author verified the similarity in relationship between innovative product perception and the product acceptance through acceptance-level model which showed that acceptance intervened with the behavior. In other words, as Gatigon and Robertson (1985) indicated, for highly involved products with big innovation acceptance cost in the customer’s point of view, rather than having acceptance right after perceiving the products characteristics, it means acceptance intention happens in order after perceiving the products characteristics and constructing the behavior towards the product. Third, the size of the perceived innovation is proven to give a positive effect on the product behavior and acceptance intention. But, even though the product innovative perception from the consumer gives a positive effect on the product acceptance intention, after a certain level the acceptance intention doesn’t increase proportionately to the size of innovation perception but the size gradually decreases. Practically, for innovative products, the company can lead the market on their own, only if the products released to the market are perceived in the consumers’ point of view, form a behavior, and go through a product acceptance process are considered, fact that consumer’s point of view for innovative product development and introducing process is important. Fourth, the two studies (WOM and Advertising) provide consistent support for the proposition that firms should start new product communications with WOM, and then continue with advertising. This strategy generates higher levels of consumer awareness and greater intentions of adopting the new product, compared to starting with advertising. However, nowadays firms can easily promote WOM communication. The internet provides numerous avenues through which to share consumers’ views, preferences or experiences with others (Trusov et al., 2009), and companies need to be aware of these. Furthermore, starting a communication strategy with WOM in a new product launch generates product-related WOM. This result is important since the higher the volume of WOM, the faster consumers’ adoption of the new product (Shen & Hahn, 2008). As a conclusion, the results reveal that product benefit, technological capabilities, consumption pattern change and word of mouth have all positive effect on the perception of innovativeness. Attitude toward a product turned out to play mediating role between perceived innovativeness and intention of adoption. In addition, the outcome of this investigation indicated that there were no significant cultural differences between respondents in USA and China in regard to the adoption of a smartphone. The survey results that would provide insight into key factors that affect the evaluation of new products rather than to provide a comprehensives list of correlated with product success. That is very valuable implication for smartphone manufacturers to develop customized R&D and marketing strategies. 6. Limitations and future research Future research should address the limitations apparent in the current study. The author measured information about searching for the new product very early on, at the awareness stage. Some consumers had not yet considered looking for information about the new product at this stage. Thus, it would be interesting to measure this variable at a more advanced stage of new product adoption. To generalize the model on innovation perception factors in consumer’s view, additional research needs to apply different products, different countries, and different consumer groups on the research model. Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 15 of 18 Funding The author received no direct funding for this research. Author details Jong Seok Kim 1 E-mail: [email protected] 1 Responsible for Academic-Industrial Cooperation Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon-Si 16419, Republic of Korea. Disclosure statement No potential conflict of interest was reported by the author(s). Citation information Cite this article as: An empirical comparison between China and USA market on smartphone adoption, Jong Seok Kim, Cogent Business & Management (2022), 9: 2036309. References Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: Replication. MIS Quarterly, 16(2), 227–247. https://doi.org/10.2307/249577 Ajzen, & Fishbein, M. (1980). Attitudes and predicting behavior. Prentice-Hall Inc. Balachandra, R., & Friar, J. H. (1997). Factors for success in R&D projects and new product innovation: A contextual framework. IEEE Transactions on Engineering Management, 44(3), 276–287. https://doi. org/10.1109/17.618169 Bauer, & Greyser. (1968). Advertising in America: The consumer view. Harvard Business. Bentler, P. M. (1983). simultaneous equations as moment structure models: With an introduction to latent variable models. Journal of Econometrics, 22(1–2), 13–42. https://doi.org/10.1016/0304-4076(83)90092-1 Bone, P. F. (1995). Word of mouth effects on short term and long-term product judgements. Journal of Business Research, 32(3), 213–223. https://doi.org/10. 1016/0148-2963(94)00047-I Brown, J. J., & Reingen, P. H. (1987). Social ties and word-ofmouth referral behavior. Journal of Consumer Research, 14(3), 350–362. https://doi.org/10.1086/209118 Buttle, F. A. (1998). Word-of-mouth: Understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), 241–254. Chung, D., & Chun, S. G. (2011). An exploratory study determining factors for the smartphone selection decision. Issues in Information Systems, 12(1), 291–300. https://doaj.org/article/ e4c705bfa0184098be74e6f69313d218 Darawong, C. (2018). Dynamic capabilities of new product development teams in perfoming radical innovation projects. International Journal of Innovation Science, 10(3), 333–349. https://doi.org/10.1108/IJIS-072017-0060 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–339. https:// doi.org/10.2307/249008 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. https://doi.org/10.1287/mnsc.35.8.982 Dellarocas, A. N., and Zhang, X. (2004). Exploring the value of online reviews to organizations: Implications for revenue forecasting and planning. International Conference on Information Systems (ICIS), 12–31. http://aisel.aisnet.org/icis2004/30 Dewar, R. D., & Dutton, J. E. (1986). The adoption of radical and incremental innovations: An empirical analysis. Management Science, 32(11), 1422–1433. https://doi.org/10.1287/mnsc.32.11.1422 Engel, J. E., & Blackwell, R. D. (1982). Consumer behavior (4th ed.). The Dryden Press. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobserable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/ 10.1177/002224378101800313 Ganji, E. N., Shah, S., & Coutroubis, A. (2018). An examination of product development approaches within demand driven chains. Asia Pacific Journal of Marketing and Logistics, 30(5), 1183–1199. https:// doi.org/10.1108/APJML-02-2018-0042 Gatigon, H., & Robertson, T. S. (1991). A propositional invertory for new diffusion research. Journal of Consumer Research, 11(March), 849–867. https://doi. org/10.1086/209021 Ghose, A., & Ipeirotis, P. G. (2007). Designing novel review ranking systems: Predicting usefulness and impact of reviews. Proceedings of the International Conference on Electronic Commerce (ICEC), Minneapolis, ACM Press, New York, NY, pp. 303–309. Godes, & Mayzlin, (2004a). Firm-Created word-of-mouth communication: A field-based quasi-experiment, INFORMS, HBS Marketing Research Paper, No. 04-03. Godes, & Mayzlin, (2004b). Firm-created word-of-mouth communication: A field-based quasi-experiment, HBS marketing research, INFORMS, Paper No. 04-03. Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex system look at the underlying process of word of mouth. Marketing Letters, 12(3), 211–223. https://doi.org/10.1023/A:1011122126881 The Guardian. (2021). www.theguardian.com Guseo, R., & Guidolin, M. (2010). Cellular automata with network incubation in Information technology diffusion. Physica A, 389(12), 2422–2433. https://doi. org/10.1016/j.physa.2010.02.007 Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis with readings (5th ed.). Prentice Hall. Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of Marketing Research, 38(August), 362–375. https://doi. org/10.1509/jmkr.38.3.362.18861 Hoffman, K. D. (2005). Marketing principles and best practices/K. Huh, Y. E., & Kim, S.-H. (2008). Do early adopter upgrade early? Role of post-adoption behavior in the purchase of next-generation products. Journal of Business Research, 61(1), 40–46. https://doi.org/10.1016/j. jbusres.2006.05.007 John, M. M. (2012). Influence of product attributes on mobile phone preference among university students: A case of undergraduate students. 1(6), 10–16. Joreskog, & Sorbom. (2019). Structural equation modeling for analysing passengers, guide to the program and applications. SPSS, Inc. Kim, K. Y., & Kang, Y. C. (2001). Analysis of structural equation model. Freedom Academy. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333. https://doi.org/10.1007/ s10796-008-9073-8 Lavidge, R. J., & Steiner, G. A. (1961). A model for predictive measurement of advertising effectiveness. Journal of Marketing, 25(6), 59–62. https://doi.org/10. 1177/002224296102500611 Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 16 of 18 Lee, S., & Johnson, Z. S. (2017). The effect of new product design and innovation on South Korean consumer’s willingness to buy. Asia Pacific Journal of Marketing and Logistics, 29(1), 98–113. https://doi.org/10.1108/ APJML-06-2015-0093 Leonard-Barton. (1985, November). Implementing new technology. Harvard Business Review, 63, 6. https:// hbr.org/1985/11/implementing-new-technology López, Sicilia, Manuela, M., & Maria, M. (2013). How WOM marketing contributes to new product adoption. European Journal of Marketing, 47(7), 1089–1114. https://doi.org/10.1108/03090561311324228 Montoya-Weiss, M. M., Calantone, R., López, M., & Sicilia, M. (1994). Determinants of new product performance: A review and meta-analysis. The Journal of Product Innovation Management, 11(5), 397–417. https://doi.org/10.1111/1540-5885.1150397 Mordor Intelligence. (2021). www.mordorintelligence.com Morrissey, & Brian. (2009, November 30). Brand in Hand: IPhone Apps Put Brands in Hands. Adweek, 50(42), 10–12.https://www.adweek.com/brand-marketing/ iphone-apps-put-brands-hands-106730/ Nam, S.-H., & Kim, S. (2004). Across the threshold: Role of performance and compatibility in innovative new products’ market penetration. Industrial Marketing Management, 33(8), 689–699. https://doi.org/10. 1016/j.indmarman.2004.03.001 O’Connor, G. C., & Veryzer, R. W. (2001). The nature of market visioning for technology-based radical innovation. Journal of Product Innovation Management, 18(4), 231–246. https://doi.org/10. 1111/1540-5885.1840231 Okazaki, S. (2004). How do Japanese consumer perceive wireless ads ? International Journal of Advertising, 23 (4), 429–454. https://doi.org/10.1080/02650487. 2004.11072894 Olshavsky, R. W., & Spreng, R. A. (1996). An exploratory study of the innovation evaluation process. The Journal of Product Innovation Management, 13(6), 512–529. https://doi.org/10.1111/1540-5885.1360512 Onkvisit, S., & John, J. S. (1989). Product lifecycle and product management. Greenwood Press, Inc. Pai, S. (2007). The impact of word-of-mouth on purchase decisions: The case of motion pictures. Boston University. Putzer, G. J., & Park, Y. (2010). The effects of innovation factors on smartphone adoption among nurses in community hospitals. Perspectives in Health Information Management, 7(1), 1–20.https://www. ncbi.nlm.nih.gov/pmc/articles/PMC2805554/ Rahim, A., Safin, S. Z., Kheng, L. K., Abas, N., & Ali, S. M. (2016). Factors influencing purchasing intention of smartphone among university students. Procedia Economics and Finance, 37, 245–253. https://doi.org/ 10.1016/S2212-5671(16)30121-6 Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innovation: A cross-cultural approach (2nd ed.). The Free Press. Rogers, E. M. (1995). Diffusion of Innovation (4th ed.). Free Press. Sanakulov, N., & Karjaluoto, H. (2017). A cultural comparison study of smartphone adoption in Uzbekistan, South Korea and Turkey. International Journal of Mobile Communication, 15 (1), 85. https://doi.org/10. 1504/IJMC.2017.080579 Shen, W., & Hahn, J. (2008). Impact of online word-ofmouth on the market for consumer goods – The interplay between adoption rate, product market life and market size. working paper, Purdue University, West Lafayette, IN, 15 October. Slama, M., & Wolfe, R. (1999). Consumption as self-presentation: A soci-analytic interpretation of Mrs. Cage. Journal of Product Marketing, 63(4), 135–138. https://doi.org/10.2307/1251979 Sohn, S. Y. (1996). Growth curve analysis applied to ammunition deterioration. Journal of Quality Technology, 27(4), 71–80. https://doi.org/10.1080/ 00224065.1996.11979638 Sohn, S. Y., & Moon, T. H. (2003). Structural equation model for predicting technology commercialization success index (TCSI). Technological Forecasting & Social Change, 70(9), 885–899. https://doi.org/10. 1016/S0040-1625(03)00004-0 Sohn, Y. S., & Ahn, K. H. (1997). New product adoption model incorporating the effect of word of mouth and advertising: A micro modeling approach. Korean Journal of Marketing, 12(1), 157–181. https://aca demic.naver.com/article.naver?doc_id=36543966 Son, S. H., Choi, Y. J., & Hwang, H. S. (2011). Understanding acceptance of smartphone among early adopters using extended technology acceptance model. Korean Journal of Journalism & Communication Studies, 55(2), 227–251. https:// scienceon.kisti.re.kr/srch/selectPORSrchArticle.do? cn=NART69849397&dbt=NART Songpol, K., Bruner, G. C., & Al-Shuridah, O. (2009). The role of social influence on adoption of high-tech innovations: The moderating effect of public/private consumption. Journal of Business Research, 62(7), 706–712. https://doi. org/10.1016/j.jbusres.2007.04.014 Steenkamp, J. B. E. M., & Gielens, K., & Steenkamp and Gielens. (2003). Consumer and market drivers of the trial probability of new consumer packaged goods. Journal of Consumer Research, 30(December), 368–384. https://doi.org/10.1086/378615 Sweeney, Sweeney, J., Soutar, G., & Mazzarol, T. (2014). Factors enhancing word-of-mouth influence: Positive and negative service-related messages. European Journal of Marketing, 48(1/2), 1–24. https://doi.org/ 10.1108/EJM-06-2012-0336 Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information System Research, 6(2), 144–176. https:// doi.org/10.1287/isre.6.2.144 Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(3), 90–102. https://doi.org/ 10.1509/jmkg.73.5.90 Tsang, M. M., Ho, S.-C., & Liang, T.-P. (2004). Consumer attitude toward mobile advertising: An empirical study. International Journal of Electronic Commerce, 8 (3), 65–78. https://doi.org/10.1080/10864415.2004. 11044301 Tsiros and Mittal. (2000). A model of its antecedents and consequences in consumer decision making. Journal of Consumer Research, 26, 4. https://doi.org/10.1086/ 209571 Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environment. Administrative Science Quarterly, 31(3), 439–465. https://doi.org/10.2307/2392832 Van Biljon, J., & Kotzé, P. (2007). Modelling the factors that influence mobile phone adoption. Proceedings of the 2007 Annual Research Conference of the South AfricanInstitute of Computer Scientists and Information Technologists on IT Research in Developing Countries (pp. 152–161). New York, NY: ACM Press. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi. org/10.2307/30036540 Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 17 of 18 Veryzer. (1998b). Key factors affecting customer evaluation of discontinuous new products. The Journal of Product Innovation Management, 15(2), 136–150. https://doi.org/10.1111/1540-5885.1520136 Wansink, et al. (1998). Perceived probability of food waste: Influence on consumer attitude. Journal of Retailing and Consumer Service, 42, 11–21. Winer, R. S. (2007). Marketing management. New York University. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729. https://doi.org/10. 1016/j.im.2004.07.001 Zanot. (2015). Public attitudes toward advertising: The American experience. International Journal of Advertising, 3–15. https://doi.org/10.1080/02650487. 1984.11104995 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Cogent Business & Management (ISSN: 2331-1975) is published by Cogent OA, part of Taylor & Francis Group. Publishing with Cogent OA ensures: • Immediate, universal access to your article on publication • High visibility and discoverability via the Cogent OA website as well as Taylor & Francis Online • Download and citation statistics for your article • Rapid online publication • Input from, and dialog with, expert editors and editorial boards • Retention of full copyright of your article • Guaranteed legacy preservation of your article • Discounts and waivers for authors in developing regions Submit your manuscript to a Cogent OA journal at www.CogentOA.com Kim, Cogent Business & Management (2022), 9: 2036309 https://doi.org/10.1080/23311975.2022.2036309 Page 18 of 18