Consumer preferences for mobile Internet: a comparative cross-national mixed methods study.
The literature in this area, depending on the interests of different researchers, has been focused on technology diffusion (Chu & Pan, 2008; Liu & Li, 2010), technology adoption (Hong, Thong, & Tam, 2006; Shin, 2007; Zhou, 2011), quality of service (Vlachos et al., 2011), users and interface (Lee, 2009), the industry and the players (Fabrizi & Wertlen, 2008), and the success factors of the service (Kim et al., 2004; Shin, 2009). The technology adoption model can be applied to mobile Internet, which is a new form of information technology (Hong et al., 2006; Shin, 2007; Zhou, 2011), and because mobile Internet is also a service, many research topics in the field of service (e.g., quality of service, customer satisfaction, and customer loyalty) can be applied to it. In addition, mobility, by nature, is a major topic in the field of context-based human computer interaction (Lee, 2009; Lee, Kim, Choi, & Hong, 2010). In particular, some scholars have focused on mobile Internet service providers, regulation bodies, and policy (Fabrizi & Wertlen, 2008; Funk 2011; Lembke, 2002). Therefore, when studying consumer preference in mobile Internet use, these different research areas can be adopted and integrated.
The objective in this paper was to clarify current consumer preferences in mobile Internet use and to predict the evolution of mobile Internet. In previous studies of the consumer's perspective, researchers have primarily used quantitative approaches. However, as the most important factors for industry managers are responsiveness and adaptiveness to the practical demands of the research problem, we reasoned that a mixed method of analysis would be the most appropriate (Gelo, Braakmann, & Benetka, 2008; Greene, Caracelli, & Graham, 1989). We therefore used both qualitative and quantitative analyses in this study.
Background of Mobile Internet Services
Mobile Internet Service Category
In this study, we explored whether or not consumers perceive mobile Internet as a service. Previous researchers have considered mobile Internet as a single concept without considering the reason(s) for mobile Internet use. In other words, previous researchers did not differentiate between, for example, the intent to use a weather information search service or an email service (Liu & Li, 2010; Shin, 2007; Vlachos et al., 2011; Zhou, 2011). Such an approach can be misleading because the assumption is made that mobile Internet is adopted regardless of the service needed.
Researchers who have studied the service category of mobile Internet have used different methods. Lee, Kim, and Kim (2005) classified mobile Internet into utilitarian services and hedonic services, while Yang (2007) categorized mobile Internet into information pull, information push, transaction, and access. Lee (2009) showed that different categories of mobile Internet service (communication, personalization, entertainment, and information) led to different perceived fulfillments of expectations. We therefore proposed the following hypotheses:
Hypothesis 1: Consumers will perceive each category of mobile Internet service differently.
Hypothesis 2: Consumers' preferences will differ for each category.
We used focus group interviews (FGIs) to determine consumers' perceptions of mobile Internet service and conducted a quantitative analysis to investigate consumers' preferences for the categories revealed in the FGIs.
Comparisons among Countries
Researchers have compared mobile Internet use in different countries. For instance, Tee and Gawer (2009) sought to explain why i-mode did not succeed in Europe despite its previous success in Japan, in relation to the architecture industry. Kim et al. (2004) and Shin (2009) compared mobile Internet use in different countries and identified the differences among them, and Vlachos et al. (2011) compared the perceived quality of mobile Internet in a number of countries. The role cultural characteristics have in mobile Internet use has also been studied (Lee et al., 2010). Therefore, after reviewing the existing literature, we proposed the following hypotheses:
Hypothesis 3.1: Consumers' preferences for mobile Internet services will differ among countries.
Hypothesis 3.2: The ways in which mobile Internet is used will vary country to country according to end user preferences.
Adopter Category and Consumer Attitudes Toward Products
Applying Rogers' (2003) theoretical framework, we classified the participants into five adopter categories: (a) innovators, (b) early adopters, (c) early majority, (d) late majority, and (e) laggards. This not only categorizes adopters, but also classifies the members of a social system on the basis of innovativeness (Rogers, 2002). Using this classification, we proposed the following hypothesis:
Hypothesis 4.1: Adopter categories will differ among countries.
Attitudes can be conceptualized as mental states and a frame of mind of liking or disliking an object that guides the way a person responds to it (Kotler & Keller, 2012, p. 190: see also Aaker, Kumar, Day, & Leone, 2010). Attitudes arise from the characteristics that consumers attribute to a product (Verbeke & Viaene, 1999). As our focus in this study was on the mobile phone as a product and we assumed that attitudes arise from the characteristics that end users attribute to the mobile phone, we proposed the following hypothesis:
Hypothesis 4.2: Consumer attitudes toward mobile phones will differ among countries.
In this study, we used a two-phase mixed method approach (from qualitative to quantitative), labeled by Creswell (2003) as the sequential exploratory design.
Focus Group Interview
We conducted interviews between March 26, 2007, and April 2, 2007, in Paris, Frankfurt, Milan, and London, and targeted individuals aged 18 to 49 years who used wireless Internet twice or more per month via a mobile phone. Four groups of either seven or eight people were sampled from each country, resulting in a total of 16 groups (115 persons). A screen-out process of thank and terminate was used to separate different groups. These four countries were selected because they account for a major portion of the handset demand in Europe, are targets of early release of mobile phones, and are the homes of large mobile operator companies (Vodafone, Orange, and T-mobile).
The FGI included unstructured and open-ended questions, which were developed with the assistance of a panel of mobile communication experts. In the questionnaires, participants were asked to provide words, feelings, images, or services that they associated with the term "mobile Internet", what they considered to be the most frequently used mobile Internet service, which mobile Internet service they were willing to use in the future, their level of satisfaction with mobile Internet, whether their needs were unmet by mobile Internet, and the image that they associated with the mobile Internet user. The question guidelines were devised in English and then translated into the respective languages for moderators (who were also given the English version).
All the FGIs were conducted in the local language and then translated into English. The moderators were bilingual experts (English and local language) and were given the questionnaire beforehand to familiarize themselves with the questions. A premeeting was conducted a day before each FGI to confirm that the moderators understood the meaning of each term and question.
The FGIs were voice recorded with the consent of all the participants, and the recorded files were transcribed for analysis. A traditional content and thematic analysis and NVivo 7 were used for coding. The traditional analysis method used was based on Neuman's (2006) framework, where codes from qualitative data are mapped thematically (Corbin & Strauss, 1990; Renzaho, McCabe, & Sainsbury, 2011). NVivo 7 is a qualitative analysis tool that has become of interest to various scholars (Zapata-Sepulveda, Lopez-Sanchez, & Sanchez-Gomez, 2012). Two moderators coded the results using NVivo 7, while the other two adjusted and confirmed the coding.
We used an online survey to collect the data for quantitative study. Web-based data collection methods have been acknowledged as being efficient and useful for their low cost and fast response rate, and for providing relatively more complete information compared to some other common methods (Mehta & Sivadas, 1995; Stanton, 1998), which makes them very attractive for international marketing purposes (Craig & Douglas, 2001; Ilieva, Baron, & Healey, 2002).
Questionnaire items about consumer attitudes toward mobile phones were constructed based on reviews of the relevant literature (Shim, Ahn, & Shim, 2006; Tan, Chong, Loh, & Lin, 2010) and revised by industry experts (directors of mobile network operators, web portal and browser engineers, and researchers at global research companies). The questionnaire was also verified with local salespeople and shop dealers to ensure content validity.
A pretest was conducted with 210 users. The data obtained were analyzed using Cronbach's alpha to determine the reliability of the questions, and factor analysis was used to test for convergent validity. Using the Kaiser-Meyer-Olkin measure of sampling adequacy we obtained a value of .724 (greater than the recommended minimum of .5; Hair, Black, Babin, & Anderson, 2009, p. 104), implying that factor analysis is appropriate. The 19 questionnaire items were classified into six factors that explained 67.71% of the variance. The six factors of consumers' attitudes toward mobile phones are summarized as follows: Mobile phones (a) should be the latest model, (b) are a part of fashion and style, (c) are just a means of communication, (d) are tools for business, (e) should have various functions, and (f) should be affordable. All Cronbach's alpha values exceeded the commonly accepted minimum of .7, indicating that the questionnaire was reliable. In addition, all factor loadings ranged between .687 and .876 (well above the threshold value) thereby indicating that the convergent validity was acceptable (Fornell & Larcker, 1981).
We conducted the survey in Paris, Frankfurt, Milan, and London following the FGIs. The target population was persons aged 19 to 44 years who possessed a mobile phone and who had made a personal decision to buy a mobile phone. People using the personal information management system (PIMS), email, multimedia message service/short message service (MMS/SMS), or Internet on their phones were selected to participate in the survey. People who had participated in a mobile phone or mobile service-related survey within the last six months and who had family members, relatives, or friends working in survey-related, advertisement, consulting, media, or mobile phone businesses were excluded. These screening processes were performed to resolve issues related to random walk-ins and to prevent respondents participating more than once (Deutskens de Ruyter, Wetzels, & Oosterveld, 2004). In each country 250 people took part in the survey (total number of participants = 1,000). The online survey was conducted and managed by a global agency, with a response rate of 12%. The respondents were given one week to respond to the virtual survey, which gave them sufficient time to respond (Ilieva et al., 2002).
The codes from the FGI script (using NVivo 7) derived the frequency and essential categorical data of each service. A chi-square test and correspondence analysis were conducted.
Correspondence analysis is an exploratory, nonparametric procedure that aids visual interpretation of relationships among categorical variables by providing a comprehensive view of the variables in a contingency table, thereby allowing effective interpretation data (Hair et al., 2009, p. 589). When this method is used, rows that are close to one another and also the column characteristics of each row can be identified. However, although, in correspondence analysis, categorical data is analyzed in a way similar to principal components analysis (Tether & Tajar, 2008), it is still an exploratory technique and does not guarantee the statistical significance of the relationship (Hair et al., 2009, p. 598). Despite this shortcoming, we judged it to be an appropriate technique for comparing mobile Internet usage among countries, and therefore adopted it in our study.
[FIGURE 1 OMITTED]
The aforementioned research process is depicted in Figure 1 and the mapping of hypotheses, data source, and methodologies are depicted in Table 2.
Verifying Hypothesis 1
Traditional content and thematic study. In this study we found that our participants perceived mobile Internet as a service (rather than simply a form of technology) that allowed them to find relevant information anytime, anywhere. The various service categories were deduced from thematic mapping of the qualitative data, which employs a bottom-up analysis of the data and allows specific categories to emerge (Corbin & Strauss, 1990). We started by discussing emerging themes that can act as service categories in the data. Themes proposed by a minority of moderators (two or fewer) were excluded if they were not confirmed by the others, who reviewed the transcripts independently and discussed the relevance of themes at a second meeting. Ultimately, six service categories were identified: real-time search, email communications, downloads, maps, blogging, and commercial transactions.
Deducing service categories using NVivo 7. The data from the FGIs were analyzed using NVivo 7 to ensure that the scripts were exclusive and transparent. Using NVivo 7 for analysis also demonstrates that both quantitative and qualitative approaches can be integrated or conducted in parallel.
Analysis with NVivo 7 resulted in 125 significant nodes and the total number of raw data codes that were mapped to each node was 1,094. Among them, about 389 (35.6%) codes were mapped to mobile Internet service categories, followed by mobile phone key buying factor (20.8%), stationary Internet (18.8%), quality of mobile Internet service (15.3%), mobile Internet user (5.1%), mobile Internet context (time, place, or occasion), and mobile Internet characteristics (1.4%).
Mobile Internet service, with a share of 35.6%, was further divided into six services. Real-time search (a search in real-time) had the greatest number of codes (31.4%), followed by content downloads with mobile Internet, email communications, commercial transactions, maps, and blogging (see Table 4). The most popular type of content that users accessed using real-time search was traffic information, followed by location information, movie information, and weather information.
These results are consistent with Hypothesis 1 in that consumers distinguished among different mobile Internet services. However, it is important to note that the number of codes mapped to a theme does not necessarily relate to preference. Therefore, a quantitative study was needed to confirm whether consumers' preferences differed for each category (Hypothesis 2).
Verifying Hypothesis 2
The online survey was used to measure preferences for the six service concepts by asking participants: "Which one do you like best among the six mobile Internet services?" Real-time search and website surfing were not distinguished from one another in the FGI, but were distinguished from one another in the online survey.
Map service was excluded from the online survey because many participants perceived map service as a real-time search for location. We found that participants preferred website surfing (22.9%), followed by real-time search (21.4%), email communications (20.8%), commercial transactions (15.7%), downloads (13.0%), and blogging (6.3%). As predicted, consumers' preferences differed for each service category. This suggests that a search service can evolve into website surfing activities as the network evolves (3G or later) and a fixed payment scheme for unlimited usage is devised. In addition, it is predicted that the commercial transaction concept, which had a low comment frequency in the FGI, will be one of the most frequently used services in the future.
Verifying Hypothesis 3
Chi-square test: Consumers' preferences across the four countries. To identify the relationship between countries and mobile Internet services, we composed a two-way contingency table. The chi-square test results revealed that the two variables (services and countries) are not independent, [chi square] (15, n = 389) = 61.91, p < .001, (FGI coding tables from NVivo 7), [chi square] (15, n = 847) = 50.61, p < .001 (online survey data). However, it was difficult to identify a set of interdependent mobile Internet services and to investigate the association between different services and different countries.
Correspondence analysis on FGI coding tables from NVivo 7 SW: Six services and four countries. Exporting coding tables from NVivo version 7 to Microsoft Excel and to SPSS version 19 allowed us to conduct Pearson's chi-square tests and correspondence analyses. A singular value represents the proportion of variance due to each dimension, and the value must exceed .2 for the dimension to be accepted as viable (Hair et al., 2009, p. 598).
Three dimensions were drawn from the correspondence analysis. The first dimension accounted for 57.2% of the chi-square value and the second dimension accounted for an additional 41.8%. Therefore, the two dimensions together predict 99.0% of the chi-square value and thus are considered adequate approximations of the chi-square value (see Table 5).
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
The relationship between countries and mobile Internet services is displayed in Figure 2. Categories that appear together are more closely associated than are those that are further apart. Real-time search, download service, email communications, and map service have relatively low dimension 1 scores, whereas commercial transactions and blogging service have high dimension 1 scores. Download service and real-time search were more common among participants from France and Italy, and participants from the UK used mobile Internet mainly for commercial transactions and blogging services.
Verifying Hypothesis 4
Correspondence analysis of quantitative online survey: Relationship between adopter categories and countries. The adopter categories were partitioned according to Rogers' (2003) theoretical framework (innovators, early adopters, early majority, late majority, and laggards). Participants from Italy were classified as innovators, participants from Germany were classified as early adopters, participants from the UK were classified as early majorities, and participants from France were classified as laggards. Since different adopter categories have different communication behaviors (Rogers, 2003, p. 22), such associations could be important indicators of mobile Internet diffusion.
Correspondence analysis of quantitative online survey: Relationship between consumer attitudes and countries. We studied how consumer attitudes toward mobile phones differed among participants from different countries. As can be seen in Figure 4, participants from Italy and Germany associated mobile phones with function and affordability, those from France associated mobile phones with fashion and style, and those from the UK associated mobile phones with new technology.
Table 6 contains a summary of the relationships among country, mobile Internet service, adopter category, and consumer attitudes toward mobile phones.
From the results, it can be concluded that the end users who participated in our study classify mobile Internet into different service categories and preferences because these service categories differed among countries. It can also be concluded that participants from different countries use different mobile Internet services, which is consistent with the observation that adopter categories and consumers' attitudes differed among countries.
In recent studies on mobile Internet consumer perspectives researchers have focused on developing a research model based on previous literature and testing the statistical significance of the model in mobile Internet use (Wang & Wang, 2010; Zhou, 2011). However, we decided to use a qualitative approach to explore the issues and then employ quantitative methods to verify the results obtained qualitatively.
The results of this research provide a direction for planning mobile Internet service and mobile phone design, and allow an estimation of the diffusion speed of mobile Internet in a country. The observation that people from Italy and Germany were mainly innovation adopters indicates that mobile Internet in Italy and Germany may diffuse relatively quickly in these markets. In addition, consumers' attitudes in Italy and Germany suggest that function and low prices are important. This suggests that mobile Internet service tariffs and device prices are important drivers of market diffusion in these countries. On the other hand, although mobile Internet diffusion in the UK may not be quick as compared to Italy and Germany, compared with those countries, in the UK mobile Internet use is driven more by new technology and innovative service than it is by price. These results suggest that Italy and Germany may lead mobile Internet diffusion but the price of services and devices can be an important factor for consumers in these countries. In the UK, on the other hand, consumers are expected to be easy adopters of mobile Internet service with high and new technology.
Although our hypotheses were confirmed, this study had limitations. The quantitative data used to complement the qualitative analysis was categorical data (frequency). Thus, the data analysis does not validate a specific research model but only verifies the independence of the variables and depicts associations among two or more categorical variables. On the other hand, the strength in this research lies in the fact that we employed correspondence analysis, an exploratory nonparametric technique that is compatible with both quantitative and qualitative analyses.
In conclusion, in this study we identified types of mobile Internet service, consumers' preferences for the services, and established how preferences for services differed among countries by using a mixture of quantitative and qualitative analyses. We also found that innovation adopter categories and consumer attitudes toward mobile phones differed according to countries. These results have implications for network operators and mobile phone developers hoping to break into these markets. Our study results also confirm that quantitative data can complement qualitative data. Future researchers could pursue this by developing theories based on the results of qualitative analyses and verify the results with quantitative studies.
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SUNGBUM KIM AND TAEYONG YANG
Korea Advanced Institute of Science and Technology
Sungbum Kim and Taeyong Yang, Graduate School of Innovation and Technology Management, Center for Science-based Entrepreneurship, Korea Advanced Institute of Science and Technology. This research was supported by a grant from the Korea Advanced Institute of Science and Technology, Center for Science-based Entrepreneurship Grant program.
Correspondence concerning this article should be addressed to: Sungbum Kim, Graduate School of Innovation and Technology Management, Center for Science-based Entrepreneurship, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea. Email: firstname.lastname@example.org
Table 1. Construct, Cronbach's Alpha, and Factor Loadings: Consumer Attitudes Toward Mobile Phones Construct Number of Cronbach Factor items alpha loadings Latest model 5 .81 .80, .78, .72, .71, .69 Fashion and style 3 .78 .84, .83, .81 Communication method 4 .77 .81, .75, .75, .71 Tools for business 3 .75 .84, .80, .79 Various functions 2 .77 .88, .87 Be affordable 2 .77 .87, .86 Table 2. Mapping of Hypotheses, Data Source, and Methods of Analysis Hypothesis Data Methods of analysis Hypothesis 1: Consumers will Focus group Thematic and perceive each category of interview content analysis mobile Internet service with NVivo 7 differently. Hypothesis 2: Consumers' Online survey Descriptive preferences will differ statistics for each category. Hypothesis 3.1: Consumers' FGI coding tables Chi-square test preferences for mobile from NVivo 7 Internet services will differ among countries. Hypothesis 3.2: The ways in FGI coding tables Correspondence which mobile Internet is from NVivo 7 analysis used will vary country to country according to end user preferences. Hypothesis 4.1: Adopter Online survey Correspondence categories will differ analysis among countries. Hypothesis 4.2: Consumer Online survey Correspondence attitudes toward mobile analysis phones will differ among countries. Table 3. Summary of Themes from FGI Transcripts Theme Voice of customer Real-time search "I use it to look up restaurant numbers." When I said quick and fast I meant that it sometimes is fast in searching for things. Email "I have a Nokia 75 because I use a lot communications of email, you know, and it's quite nice, I like the way it works, I like to hold it, I think it looks good." "I travel around a lot, you know, very often I have to check my email account and its very handy to be able to do that." Downloads "I have downloaded one game Tetris but I haven't done anything since, I think it was like 5[pounds sterling] or something." "I downloaded much more content than I should have done and it cost me a fortune!" Maps "It would be good to have both map and navigation. But you need a docking station to install it in your car." It's got a better graphic, a better resolution. It must have a very high resolution for a good graph. Blogging "I go onto, like, MySpace looking for friends and maybe do up your own page." "I have one (Sky Blog) so I update it and I also visit friends' blogs too." Commercial "Things like on my PC I can do one transactions click and I have paid, if I lose my mobile then someone else can do that." "Some shopping sites have got like flash and stuff that mobile phones don't have so half the stuff you can't use anyway cause whatever website you use it has got to work." Table 4. Comparison of Results of Online Survey and FGI Mobile Focus group Online Internet interview survey service (using categories NVivo 7) Freq. Ratio Freq. Ratio Real-time search 122 31.40% 181 21.37% Downloads 102 26.20% 110 12.99% Email communications 90 23.10% 176 20.78% Commercial 30 7.70% 133 15.70% transactions Maps 25 6.40% 0 0.00% Blogging 20 5.10% 53 6.26% Web surfing 0 0.00% 194 22.90% Total 389 100.00% 847 100.00% Table 5. Determination of Dimensionality Dimension Singular Inertia Chi- Sig value square 1 .302 .091 2 .258 .067 3 .039 .002 Total .159 61.909 .000a Dimension Proportion Confidence of inertia singular value Accounted Cumulative SD Correlation for 2 1 .572 .572 .046 -.071 2 .418 .990 .044 3 .010 1.000 Total 1.000 1.000 Table 6. Mapping of Variables and Four European Countries Country Six mobile Adopter Consumer Internet categories attitudes toward services mobile phone France Real-time search, Laggard Fashion/style downloads Germany - Early adopter Function, affordability Italy Real-time search, Innovator Function, downloads affordability UK Commercial Early New technology transactions, majority model blogging
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|Author:||Kim, Sungbum; Yang, Taeyong|
|Publication:||Social Behavior and Personality: An International Journal|
|Date:||Nov 1, 2012|
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