Latent segmentation of older adults in the use of social networks and e-banking services

17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 1/16       ,  . 24 . 4, D, 2019 Contents | Author index | Subject index | Search | Home Latent segmentation of older adults in the use of social networks and e-banking services Ángel F. Villarejo-Ramos, Begoña Peral-Peral and Jorge Arenas- Gaitán Introduction. This study analyses heterogeneity in the online behaviour of elderly people. Previous research has centred on the socio-demographic segmentation of the elderly regarding their Internet use. Method. The novelty that this study adds is in determining this segmentation through variables that have been especially chosen for studies of the elderly and which are related to their utilitarian use of e-banking, compared to the more hedonic nature of using online social networks. Analysis. The sample was collected using 474 students over 55 years old enrolled in a class of experience in a University in the South of Europe (Spain). We use a latent class cluster model which is appropriate in situations of a posterior segmentation. Results. In dealing with such behaviour in situations of the elderly using these technologies, we have detected the presence of five groups or segments with highly differentiated use-related profiles concerning the variables analysed: venturesomeness, technology anxiety and selfconfidence in information and communication technologies use. Conclusion. We discover the existence of heterogeneity in the behaviour of the elderly regarding services available in Internet. The results support the idea proposed. The stereotype of the elderly cut off from technology is mistaken, as is shown by the different segments found. Introduction Throughout history technology has influenced all aspects of a person’s life, irrespective of their age. In the case of the elderly, different research has analysed the advantages of information and communication technologies (ICT) (e.g., Chen and Chan, 2014; Cresci, Yarandi and Morrell, 2010; White et al., 1999; Wood, Lanuza, Baciu, MacKenzie and Noskol, 2010). Yet, within this framework, we find little research that has tackled the use of other Internet-based applications and that can facilitate the daily life of the elderly, fostering active ageing. This concept is, according to the World Health Organization, ‘the process of optimising health opportunities, participation and safety with the aim of improving quality of life as people get older’. The term active suggests: ‘continuous participation in social, economic, cultural, spiritual and 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 2/16 civic questions, not only the capability of being physically active’ (World Health Organization, 2002, p. 79). In the current technological and globalised environment, as is indicated by the World Economic Forum (2011, p. 109) in their Global Population Ageing: Peril or Promise? ‘ICT have a fundamental role’. A bibliographic review concerning the elderly and information and communication technologies shows, that these users have higher levels of technology anxiety and less self-confidence than younger people (Czaja and Sharit, 1998; Dyck and Smither, 1994; Guo, Sun, Wang, Peng and Yan, 2013; Lam and Lee, 2006). Nevertheless, other studies (Agudo, Pascual and Fombona, 2012; Niemelä-Nyrhinen, 2007) carried out about the elderly belonging to the baby-boom generation (born between 1945 and 1955) showed differences with respect to this stereotype, as these elderly people have matured with the birth of current technologies and therefore have had the experience of using them. This work means to analyse the heterogeneity in the online behaviour of the elderly by recognising different levels of technology acceptance and use. The explanation of the difference of the online behaviour of the elderly can vary between utilitarian motivation, investigated from the use of e-banking and hedonic motivation, studied from the utilisation of online social networks sites. The differences shown in the behaviour of the elderly in these two Internet applications are described by segments differentiated by psychological characteristics, such as venturesomeness, technology anxiety and self-confidence, usually used in studies of the behaviour of elderly technology users. For the empirical development of our research, we have used a sample of elderly people who are users of ebanking and/or have active accounts in online social network sites. We have analysed their online behaviour facing both situations: 1) their normal operation with e-banking, and 2) their activity in online social network sites, exchanging and sharing information, and frequency of use. The method used is a latent class cluster model, which performs a posterior segmentation based on the results of the data analysis of the sample observed and moreover enables the identifying of the individual’s profile in each of the segments (Wilson- Jeanselme and Reynolds, 2006). The structure of this work is as follows. First, there is a theoretical review of the main variables analysed in the segmentation model. These are both the psychological variables and the different online activities of the elderly regarding the two online platforms chosen for their utilitarian or hedonistic character. In the next section, we describe the methodology chosen and justify it as a segmentation method among users with activities that are observed a posteriori. Then, we highlight the results obtained from the sample of the elderly who are users of the technologies analysed and describe the different segments which result from the application of the latent class model. Finally, there is a section of discussion of the results obtained which leads us to determine the main implications for management, and the limitations and conclusions of the investigation. Theoretical background Elderly people and their behaviour in relation to the Internet The segment of the elderly has rates of penetration of new technologies, such as Internet and mobile phones, smaller than that of the rest of the population (Peral-Peral, Arenas-Gaitán and Ramón-Jerónimo, 2013; Chung, Park, Wang, Fulk and McLaughlin, 2010; Hill, Beynon-Davies and Williams, 2008; Reisenwitz, Iyer, Kuhlmeier and Eastman, 2007). Jones and Fox (2009) find the elderly perceived the Internet more as a tool than as a leisure environment. They use technologies mainly to communicate, entertain themselves and facilitate their daily activities (Mitzner et al., 2010). Likewise, the elderly are more inclined to use technologies which are more established in the market (Chen and Chan, 2014), given that they are among late adopters of technologies and not pioneers. The motives which explain the different level of use of Internet by the elderly, compared with younger generations, are: 1) a lack of interest, probably because they do not need to use information technologies in their daily life or do not feel social pressure to do so (Iyer and Eastman, 2006; Nayak, Priest, Stuart- Hamilton and White, 2006; Reisenwitz et al., 2007); 2) due to the negative influence of formal skills to use Internet (Van Deursen and Van Dijk, 2010); and 3) because of the difference in the need of and search for information with respect to other population segments (Dennis, Merrilees, Jayawardhena and Wright, 2009; 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 3/16 Gilly, Celsi and Schau, 2012). Likewise, the elderly indicate that they do not use the Internet when the information and services which they need are available through traditional means, such as newspapers, telephones or the mail (Morrell, Dailey, Stoltz-Loike, Mayhorn and Echt, 2005) and they fear that adopting new technological tools can cause great changes in their daily routines (Kurniawan, 2008). Even so, there is a significant group of the elderly involved with the Internet and who use its tools and applications and are, mostly, those who are younger within the group of the elderly (Cresci, Yarandi and Morrell, 2010; Fox, 2004; Hogeboom, McDermott, Perrin, Osman and Bell-Ellison, 2010). Thus, chronological age in the group of the elderly explains differences in their online behaviour. Fox (2004) states that the so-called silver tsunami of those aged between 50 and 64 has little to do with inactive surfers of 80 or more. McCloskey (2006) also differentiates between the elderly by subgroups of age, finding that, with respect to e-shopping, the probability of carrying out an online transaction is less for those who are older and that they also observe a greater difficulty in their use of the Internet. In general, the probability of commitment to the Internet is reduced to half as age increases, especially within the group of elderly over 70 (Demunter, 2005). Beyond age, the previous results suggest that there must be explanatory variables of the differing behaviour of the elderly in using the Internet (Gilly et al., 2012). Some works (Meuter, Ostrom, Bitner and Roundtree, 2003; Ryu, Kim and Lee, 2009) have identified psychological variables related to age and the behaviour of the elderly in relation to the Internet such as venturesomeness, self-confidence and technology anxiety, or more related to the context specific to the elderly, such as, for example, perceived physical conditions. Psychological variables that influence online behaviour Venturesome people are moved by the wish to try out new and exciting things. They are aware that there is a risk involved in their decisions, but they are more daring. It is a question of an intrinsic motivation of the individual towards stimulation, knowledge and achievement (Clarke 2004). Venturesomeness is part of the internal locus of control (Chantal and Vallerand, 1996): the subjects perceive that the facts of their life are effects and consequences of their decisions in such a way that they enjoy facing challenging experiences. It is to be expected, therefore, that the people who have this personality dimension will be involved in new and challenging activities, such as those related to technology. For example, in the environment of ebusiness, Siu and Cheng (2001) consider different characteristics of individuals and found that those who adopt them reflect a greater level of venturesomeness than those who do not adopt them, are more predisposed to risk-taking, as well as being more interested in technological developments. In the context of the elderly, Sudbury and Simcock (2009) carried out a segmentation of 650 British people aged 50 to 79, using behavioural variables. Their results indicated that a venturesome character enables differentiating among the elderly. Hence, the segment called positive pioneers has high levels of venturesomeness, as they like to buy and try out new things out of curiosity, to be the first to do so, to comment about it to their friends and to share information. Technology anxiety is considered to be an example of the state of anxiety (Chua, Chen and Wong, 1999), a transitory state or condition of variable intensity, which fluctuates over time. It is considered that technology anxiety leads to negative impacts in the individual’s cognitive responses (Guo et al., 2013), such as the use of an Internet application, and can be mainly modified through training and experience with the technology. Technology anxiety is the main determinant at the individual level of the use of a technology (Meuter et al., 2003). Furthermore, another of its consequences is the resistance to change, given that those people with high levels of technology anxiety tend to be more concerned about unexpected technological errors (Durndell and Haag, 2002) and this is why they try to maintain the initial state (Guo et al., 2013). Self-confidence can be defined as people’s own evaluation of their capacity to carry out an activity (Dabholkar and Bagozzi, 2002), and it reflects the degree to which future users believe that they are sufficiently capable of successfully using a specific technology (Walker and Johnson, 2006). Yagil, Cohen and Beer (2013) point out that self-confidence positively influences the intention of using and use a technology. People who have a high self-confidence tend to focus on the fun and entertainment that using the technologies that they know gives them (Dabholkar and Bagozzi, 2002). The level of self-confidence is influenced by demographic variables, such as age and sex (Loibl, Cho, Diekmann and Batte, 2009). 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 4/16 The perceived physical conditions refer to the perception of people’s own physical difficulties in sight, hearing and motor systems which they face in their daily life. These perceptions can serve as internal controls or conditions which inhibit their intention to use a technology (Chen and Chan, 2011; Hough and Kobylanski, 2009). Ageing is a continuous and complex process (Mathur and Moschis, 2005; Moschis, 1994) which involves changes in the way that people interact and respond to their environment (Ryu et al., 2009). The ageing process causes gradual losses in the sensory and motor systems. The most significant changes regarding the use of computers are related to sight and hearing (Xue et al., 2012) and this involves more difficulties in their use (Phang et al., 2006). Hedonic and utilitarian motivations The utilitarian perspective in assigning value in purchasing decisions is considered as a task which people carry out seeking economic benefits (Holbrook and Hirschman, 1982). On the other hand, the hedonic perspective refers to emotional and enjoyment aspects connected to the decision made by the purchaser (Hirschman and Holbrook, 1982) that can be more influenced by aesthetic and emotional characteristics which give the product or service a high intrinsic value. For Babin, Darden and Griffin (1994), a utilitarian value is based on a mental and rational task where the purchase is more functional. On the contrary, a hedonic value of the purchase makes it more festive and fun, reflects the emotional value and considers it to be a pleasurable experience. Hedonic purchasing means an amusing and adventurous escape for the purchaser. The digital context in purchasing situations has developed proposals which tend to analyse the hedonic and utilitarian benefits in online purchasing situations or related to the acceptation and use of technologies as communication instruments. As Van der Heijden (2004) points out, when a technology provides utilitarian and hedonic benefits at the same time it will be easier to use, though it is the hedonic value which can have a greater influence on the intention of use and its acceptation. Hedonic motivation in the use of technology also influences innovative people, although when experience increases the attractiveness of the technological innovation it can cease to add hedonic value and become more a utilitarian motivation (Venkatesh, Thong and Xu, 2012). In online social network sites, whose motivation is mainly hedonic, the utilitarian value appears as the user’s experience increases. The utilitarian value is determined by achieving specific aims through carrying out specific tasks (Kim and Han, 2011). This is why functional motivation acts as a key determinant in the intention of use of a technology. In the context of e-banking, however, the technical characteristics of the system are considered to be more influential in the intention of use, these being the functional characteristics of the bank’s website (Ndubisi and Sinti, 2006). This is why the efforts to give a better usability to e-banking platforms is concentrated on purely utilitarian aspects, such as convenience, accessibility, and the availability of information in the purchasing website (Martínez-López, Pla-García, Gázquez-Abad and Rodríguez-Ardura, 2014). Even so, some investigations find that hedonic motivation, measured through perceived enjoyment, is important in determining the consumption of technological products and services (Brown and Venkatesh, 2005; Nysveen, Pedersen and Thorbjornsen, 2005; Van der Heijden, 2004). To sum up, the dual approach about hedonic or utilitarian motivation in the use of technologies and the online behaviour seems to opt differently when the technology is a more utilitarian application and one from which a rational and economic performance is expected, such as e-banking (Ndubisi and Sinti, 2006). On the other hand, the interest in considering hedonic motivations is centred more on virtual contexts related to leisure and entertainment (Suárez-Álvarez, Del Río-Lanza, Vázquez-Casielles and Díaz-Martín, 2008), in which we include the use of online social networks. Research method Description of the sample 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 5/16 The sample was collected using students over 55 years old enrolled in a Class of Experience in a University in the South of Europe (Spain). Data were collected through a survey during the months of November and December 2015, during class, using a self-administered survey questionnaire. The initial research questionnaire was developed from well-validated instruments used in previous studies. A pre-test of this questionnaire was carried out with seven students who completed the questionnaire and provided feedback regarding their opinions about the questions. This led to a revision of the questionnaire, which, in its second version was sent to the General Directorate for Quality, Research, Development and Innovation of the Regional Government of Andalusia (Spain), which ruled that it fulfilled the necessary requirements of the suitability of the ethical protocol related with the study’s aims and that is in keeping with the ethical principles applicable to this type of study. Five hundred and seventy-six questionnaires were obtained after a process of refining and eliminating the questionnaires incorrectly filled out. This led to 474 valid surveys (the response rate was 82.29%). The proportion of women of 65.4%, the mean age was 64 (an age range of 55-85), secondary studies 53.8%, and university studies 35.1%, and the socioeconomic class was mostly middle class: 80%. Measurement scales To analyse the use of e-banking we used the scale employed by Kwon and Wen (2010) whose items were anchored on a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). Regarding social networks, it was asked if respondents used them and if they had an account (no/yes) and which networks they used (Facebook, Twitter or others), and the number of networks that they used was extracted from this (0: none to 4: four networks). A total of nine banking services carried out through the Internet was presented in the questionnaire as well as nine social network tools, requesting that the frequency of use be indicated (1: no use, 2: several times a month, 3: several times a week). Three e-banking services and social network tools were selected which contained a higher average: checking bank accounts, carrying out transfers and obtaining information about my investment portfolio; making comments, showing photographs and chatting in the social network. The variables that we used to describe the latent classes or co-variables were sex, age, level of studies, as well as four psychological constructs: perceived physical condition, measured by the scale proposed by Ryu et al. (2009) and Phang et al. (2006); venturesomeness, technology anxiety and self-confidence, measured by the scale proposed by Meuter et al. (2003). The items of these four constructs were anchored on a 7-point Likert scale. To assess the constructs use of e-banking, and the four psychological co-variables, we conducted a confirmatory factor analysis using partial least squares with SmartPLS 2.0.M3. Based on the factor analysis results, we analyse convergent validity, discriminate validity, and the reliability of all the multiple-item scales (Fornell and Larcker, 1981). These factors were then introduced into the cluster model. Statistical tools For segmentation purposes, we use a latent class cluster model. First, this model performs post hoc segmentation, as the number and type of segments is determined on the basis of the results of the analysis of the data and also classifies every individual within a single segment (Wilson-Jeanselme and Reynolds, 2006). Secondly, these models describe the relation between the variable observed and enable the inclusion of additional parameters that explain the relationship between the known variables and other latent and unknown variables a priori (Vermunt and Magidson, 2005; Wedel and Kamakura, 2000). Furthermore, different studies have demonstrated their superiority over traditional cluster techniques (DeSarbo and Wedel, 1994; Rondán-Cataluña, Sánchez-Franco and Villarejo-Ramos, 2010), as they identify clusters that group cases or individuals who share interests or characteristics, which are similar and different to those grouped in other clusters and whose responses are generated by the probability distribution (Magidson and Vermunt, 2004). The statistical software used for the latent cluster segmentation was Latent Gold 4.0. Results 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 6/16 We conducted a confirmatory factor analysis, to assess the constructs (use of e-banking, perceived physical condition, venturesomeness, technology anxiety and self-confidence). Table 1: Loadings, average variance extracted (AVE), composite reliability and Cronbach’s alpha Constructs Items Loadings Perceived physical condition AVE: 0.730 Composite reliability: 0.889 Cronbach’s Alpha: 0.820 Requires me to exert more effort to perform daily activities. Limits the kind of activities that I can perform.. Causes me to have difficulty in performing daily activities 0.735 0.896 0.922 Technology anxiety AVE: 0.641 Composite reliability: 0.877 Cronbach’s Alpha: 0.813 I have difficulty understanding most technology matters. When given the opportunity to use technology, I fear I might damage it in some way. I have avoided technology because it is unfamiliar to me I hesitate to use technology for fear of making mistakes I cannot correct. 0.763 0.837 0.790 0.811 Self confidence AVE: 0.598 Composite reliability: 0.817 Cronbach’s Alpha: 0.666 I think I have more self-confidence than most people. I am more independent than most people. I often can talk others into doing something. 0.738 0.809 0.772 Venturesomeness AVE: 0.828 Composite reliability: 0.905 Cronbach’s Alpha: 0.809 I like to try new and different things. I enjoy doing new things. 0.961 0.855 Use of e-banking AVE: 0.848 Composite reliability: 0.943 Cronbach’s Alpha: 0.911 I use the Internet banking frequently. I expend a lot of time using Internet banking. I am very involved with the Internet banking. 0.855 0.918 0.931 To determine the number of clusters that best fits the data, we requested the extraction of 1 to 7 clusters. The Bayesian (BIC), Akaike (AIC) and corrected Akaike (CAIC) information criteria (based on the loglikelihood) compare the different solutions of the models, based on the fit and the simplicity (Vermunt and Magidson, 2005). The lowest values of these criteria were attained with the 5-cluster model. Regarding the goodness in classifying the cases in the clusters, the classification errors (better when closer to zero), the standard R2 regression statistic (better when closer to zero), and the approximate weight of evidence, proposed by Banfield and Raftery, 1993) (better when its value is lower) support the decision to select the 5- cluster model (Table 2). Table 2: Model summary Cluster Log-likelihood Bayesian IC (Log-likelihood) Npar* Classification errors R2 standard Approximate weight of evidence 1-Cluster -3849.5517 7816.1664 19 0 1 7990.2294 2-Cluster -3327.0551 6900.5584 40 0.022 0.9308 7326.1065 3-Cluster -3145.305 6666.4436 61 0.0424 0.9027 7330.5352 4-Cluster -3035.165 6575.5489 82 0.0532 0.8889 7463.2643 5-Cluster -2787.5664 6209.7372 103 0.0434 0.9159 7269.2739 6-Cluster -2741.6493 6247.2883 124 0.0519 0.9014 7526.5044 * Npar = the number of parameters included in the analysis: this increases as the number of clusters 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 7/16 increases. We use Wald tests to estimate the difference of the means and probabilities between classes. The indicators analysed in the model are all significant (Table 3), as shown in the p-value of the Wald statistic (below 0.05). Therefore, each indicator contributes significantly to the ability to discriminate between clusters. The R2 measure for each indicator, defined as the ratio of variation among classes and the total variation of the analysed variable, displays how much variance of each indicator is explained by the 5-cluster model. Table 3: Models for indicators Indicator Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Wald p-value R2 E-banking use 0.919 -0.938 0.593 -0.029 -0.545 1882.096 1.1E-406 0.627 E-b checking bank accounts 1.733 -1.183 1.866 -1.639 -0.776 109.127 1.10E-22 0.580 E-b transferring accounts 2.115 -0.607 2.100 -2.131 -1.476 64.966 2.60E-13 0.393 E-b information of portfolio 1.615 -0.681 1.563 -2.260 -0.237 55.580 2.50E-11 0.292 No use of social networks -1.715 1.136 0.994 0.501 -0.916 95.608 8.50E-20 0.573 Use of social networks 1.715 -1.136 -0.994 -0.501 0.916 95.608 8.50E-20 0.573 No social network account -1.693 0.875 0.948 1.018 -1.147 116.619 2.80E-24 0.682 Has social network account 1.693 -0.875 -0.948 -1.018 1.147 116.619 2.80E-24 0.682 No. of social networks 1.781 -1.246 -1.211 -0.715 1.391 51.973 1.40E-10 0.412 Making comments 2.941 -2.054 0.451 -4.547 3.209 64.941 2.60E-13 0.515 Showing photographs 1.216 -0.588 0.151 -2.607 1.829 69.544 2.80E-14 0.283 Chatting in the network 3.024 -3.950 0.907 -3.349 3.369 30.130 4.60E-06 0.311 Socio-demographic and psychographic characteristics of individuals affect the latent variable but have no direct effect on the indicators. These covariates enable the identification of members of each cluster. Sex, age, venturesomeness and technology anxiety are significant in the model of five clusters, as shown in the pvalue of the Wald statistic (below 0.5) (Table 4). Table 4: Models for covariates Covariates Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Wald p-value Sex* Men Women 0.359 -0.359 -0.240 0.240 0.465 -0.465 -0.447 0.447 -0.137 0.137 31.544 2.40E-06 Age -0.041 0.020 -0.026 0.077 -0.030 12.321 0.015 Educational level* 0.168 -0.097 0.084 0.316 -0.470 4.725 0.320 Venturesomeness 0.351 -0.247 0.160 -0.316 0.050 18.230 0.0011 Perceived physical condition 0.018 0.069 0.162 -0.255 0.005 4.487 0.34 Self-confidence 0.027 -0.174 -0.041 -0.002 0.188 3.681 0.45 Technology anxiety -0.212 0.316 -0.093 -0.160 0.152 13.781 0.008 * Nominal variables In Table 5 we present the size of the five clusters. The profiles indicate the conditional probabilities of each dichotomous indicator. In the case of continuous indicators the averages are indicated and in the case of ordinal indicators the probabilities and means are presented. Table 5: Indicator profiles Indicators Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster size 0.260 0.252 0.179 0.171 0.136 E-banking use (mean) 0.888 -0.967 0.563 -0.059 -0.574 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 8/16 Check the balance of my accounts (mean) - no use - several times a month - several times a week 1.477 0.079 0.363 0.556 0.227 0.788 0.194 0.016 1.529 0.065 0.341 0.594 0.148 0.858 0.134 0.007 0.327 0.705 0.262 0.032 Transfer funds between accounts (mean) - no use -several times a month -several times a week 0.879 0.323 0.476 0.201 0.093 0.909 0.088 0.002 0.871 0.327 0.475 0.198 0.021 0.979 0.021 0.000 0.040 0.960 0.039 0.001 Information on my investment portfolio (mean) - no use -several times a month -several times a week 0.827 0.435 0.302 0.263 0.076 0.929 0.065 0.006 0.793 0.454 0.299 0.247 0.015 0.986 0.014 0.000 0.124 0.890 0.097 0.013 No social network use 0.024 0.880 0.847 0.674 0.108 Use of social networks 0.976 0.120 0.153 0.326 0.892 No social network account 0.092 0.945 0.952 0.958 0.231 Has social network account 0.908 0.055 0.048 0.042 0.769 Number of social networks (mean) 1.493 0.336 0.345 0.480 1.279 making comments (mean) -no use -several times a month -several times a week 1.002 0.252 0.494 0.254 0.013 0.987 0.013 0.000 0.151 0.855 0.139 0.006 0.001 0.999 0.001 0.000 1.137 0.189 0.485 0.326 showing photographs (mean) - no use - several times a month - several times a week 0.647 0.515 0.323 0.162 0.108 0.899 0.093 0.008 0.232 0.798 0.173 0.030 0.014 0.986 0.014 0.000 1.022 0.310 0.358 0.332 chatting in the network (mean) - no use - several times a month - several times a week 0.609 0.541 0.309 0.150 0.001 1.000 0.001 0.000 0.072 0.932 0.064 0.004 0.001 0.999 0.001 0.000 0.811 0.424 0.342 0.234 The values of the profiles of the indicators for the clusters are shown in Figure 1. Figure 1. Cluster profiles (IB = Internet banking; SNS = social networks) Figure 1: Cluster profiles (IB = Internet banking; SNS = social networks) The values are re-scaled to always lie within the 0-1 range, and for dichotomous variables specified as nominal (no/yes) by default only the last category is displayed. The values of significant covariates for clusters are also presented (Table 6). Table 6: Covariate profiles Covariates Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Sex* Men Women 0.496 0.504 0.206 0.792 0.540 0.459 0.201 0.799 0.225 0.773 Age 63.164 64.238 63.809 65.049 63.194 Educational level Primary Secondary University 0.037 0.544 0.354 0.155 0.491 0.269 0.092 0.461 0.377 0.121 0.428 0.306 0.113 0.513 0.297 Venturesomeness 0.237 -0.192 0.098 -0.255 0.094 Perceived physical condition -0.066 0.073 0.076 -0.140 0.068 Self-confidence 0.031 -0.099 -0.001 -0.043 0.179 Technology anxiety -0.254 0.307 -0.120 -0.090 0.189 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 9/16 Next we analyse the information on the profiles of the indicators and the covariates together in order to identify, describe and suggest a denomination for each cluster. CLUSTER 1: made up of 26% of the respondents. This is the segment which most uses e-banking and uses the three services analysed, especially to check the balance of the accounts, with a frequency of several times a week. It is the group which most uses social networks - they are present in more than one and 90% of the individuals have an account. The most common activity in such networks is carrying out comments, several times a month. With regard to the covariates, there are no differences between the number of men and women (49.6 – 50.4%, respectively), their mean age is the lowest of the five clusters, they are the individuals who have the greatest venturesomeness and have less technology anxiety. We denominate this cluster eelderly CLUSTER 2: is made up 25.2% of the sample. These people do not use e-banking or social networks. About 79% are women. The motives of non-use stem from the fact that they are individuals who are not interested in trying out new things, as well as being the segment with greater technology anxiety. We call this group fearful of technology. CLUSTER 3: almost 18% of the sample. They use e-banking, and have high values in the measures of use for the three services analysed, but 85% of this group do not use social networks. The percentage of men is 54%. They feel slightly adventurous and are not afraid of facing technology. Although the level of studies is not a significant covariate when differentiating between classes, it is worth highlighting that this group is the one with a greater percentage of university studies. Given that these individuals show an online behaviour defined by utilitarian applications (e-banking), we name it e-users for convenience. CLUSTER 4: is made up 17% of the sample. They do not use e-banking. Almost a third of this group have used social networks, though they have the lowest percentage of network accounts. Almost 80% are women and they have the highest mean age. It is the least venturesome segment and yet they are not afraid of technologies. A possible explanation of this segment is that the individuals have used social networks through family members. This would explain why they do not have an account and do not have to face the challenge of using technologies, as others do it for them. We call this group surfing with the family. CLUSTER 5: 13.6% of the sample. They do not use e-banking, but they do use social networks (89.2%). They have a high percentage of taking them into account (76.9%), are in more than one online social network and have the largest mean of use of the social network tools analysed. 77.3% are women, with a very low average age, are slightly venturesome, are not afraid of technology and this is the segment with most self-confidence. These individuals show an online behaviour that has a hedonic leaning. We call it hooked by networks. Discussion The aim of our work was to discover the existence of heterogeneity in the behaviour of the elderly regarding services available in Internet. The results support the idea proposed. The stereotype of the elderly cut off from technology is mistaken, as is shown by the different segments found. Next, we will discuss the classes found more thoroughly. The largest group is what we call e-elderly. This segment has crossed the digital gap and actively uses the Internet services we have analysed. Other authors have obtained similar results with regard to this group of individuals, calling them silver tsunami (Fox, 2004), new age (Mathur, Sherman and Schiffman, 1998) or baby boomers (Niemelä-Nyrhhinen, 2007). They are the elderly who most enjoy new experiences and interacting with technology does not produce anxiety in them. This segment will show a greater propensity to using new tools and online applications, with which they will seek hedonic benefits linked to activities of leisure and free time and utilitarian benefits with applications which facilitate their routine activities. They will therefore be an attractive segment for firms of online tourist services, mobile applications concerning leisure and culture, financial services, e-administration and health applications, among others. Another of the segments identified is that of e-users for convenience. This group uses e-banking, but does not use online social networks. The largest percentage of men is to be found in this segment. In this sense, Venkatesh and Morris (2000) find that the main motivation for men for using technology is the usefulness 17/12/2019 Latent segmentation of older adults in the use of social networks and e-banking services informationr.net/ir/24-4/paper841.html#author 16/16 © the author, 2019. Last updated: 14 November, 2019 Contents | Author index | Subject index | Search | Home