Edited by: Alessandro Vinciarelli, University of Glasgow, UK
Reviewed by: Andrej Košir, University of Ljubljana, Slovenia; Ronald Böck, Otto von Guericke University Magdeburg, Germany
*Correspondence: Carlota Lorenzo-Romero, Business Department, University of Castilla-La Mancha, Plaza de la Universidad 1, 02071 Albacete, Spain
This article was submitted to Human-Media Interaction, a section of the journal Frontiers in Psychology
†The authors are listed in alphabetical order.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The growing use of Social Networking Sites (SNS) around the world has made it necessary to understand individuals' behaviors within these sites according to different cultures. Based on a comparative study between two different European countries (The Netherlands versus Spain), a comparison of typologies of networked Internet users has been obtained through a latent segmentation approach. These typologies are based on the frequency with which users perform different activities, their socio-demographic variables, and experience in social networking and interaction patterns. The findings show new insights regarding international segmentation in order to analyse SNS user behaviors in both countries. These results are relevant for marketing strategists eager to use the communication potential of networked individuals and for marketers willing to explore the potential of online networking as a low cost and a highly efficient alternative to traditional networking approaches. For most businesses, expert users could be valuable opinion leaders and potential brand influencers.
A high degree of prosocial tendencies in human interactions has been revealed by behavioral economics research, suggesting a necessary shift from a model of selfish preferences toward social preferences—the assumption that people intrinsically are concerned about others' well-being (Caviola and Faulmüller,
From the marketing point of view, the importance of analysing the extraordinary evolution of new technologies is very useful to come to understand the evolution of consumer behavior in a cross-cultural context.
The spread of information and communication technologies has been growing so quickly that scholars have barely started to study its arrival, use, and effects on societies. Historians and other social scientists have the opportunity to examine the emergence of new and significant technologies and their effects while they are taking place—not decades or centuries after the event (Cortada,
The new social medias' provide people with bodies that are combinations of embodied and technologically mediated actions. According to Jackson and Wang (
The comparative approach allows the comprehension of the effects of factors associated with a specific trait or behavior across a range of social environments (Quinlan and Quinlan,
National culture affects the technology acceptance (i.e., computers, the Internet, mobiles) through its impact on some key variables associated with the implementation process (Baron and Hard af Segerstad,
According to different sources (e.g., ComScore Data Mine,
Once the relevance of SNS have been confirmed, both for business and individuals, the general purpose of this study is to contribute to the existing knowledge body about the interaction behavior of SNS users, and to analyse the influence of culture on SNS acceptance and use. To that end, we will focus on two European countries with clearly different usage levels of SNS, and with distinct cultural dimensions according to Hofstede (
Spain and The Netherlands represent two of the largest European countries in SNS adoption. According to Eurostat data (2014), 92% of Dutch and 66% of Spanish citizens regularly (i.e., every day or almost every day) use the Internet (Eurostat,
The huge growth of SNS has strongly attracted the interest of business strategists and marketing practitioners; publicity, research papers, and journal special issues around the subject are also increasing. Despite this growing interest of researchers in SNS as part of the marketing strategy, little academic attention has been so far placed on the nature and behavior of the online SNS user. This study is a step in the direction of mapping the SNS users by identifying market segments and profiles on the basis of socio-demographics, motivations and behavioral responses. Therefore, the purpose is to build a taxonomy of users as the first step to come to understand the behavior of this category of consumers. Segmentation in general and SNS segmentation in particular lead to various benefits for business (Dibb,
What is more, according to Sheth and Parvatiyar (
The diversity of members within social networks affects their behaviors within these informational environments (Wang and Fesenmaier,
As well as personality, another characteristic affecting the contribution level is the general participation within SNS (Wang and Fesenmaier,
Age is a personal characteristic with high influence in social media contexts. Güroğlu et al. (
Individuals also differ in their attitudes to the SNS and in their behavior when they use them. Ofcom (
Campbell et al. (
Despite the increasing importance of international market segmentation for marketing as a discipline (in particular, for international marketing), the relative importance as research topic in marketing literature remains low. Of course, a series or papers devoted to international market segmentation have appeared during the last three decades (e.g., Luqmani et al.,
An international segmentation study carried out by Bastian et al. (
Hofstede (
On this subject, Grande (
Group 1: Latin European countries, concretely Belgium, France, Spain, Portugal, and Greece, characterized by high power distance and high uncertainty avoidance, are located in the right-hand part of the positioning map. It should be pointed out that the values in these two dimensions are higher when the country is further to the right on the map.
Group 2: Anglo-Saxon countries, concretely the USA, England, and Austria. They are more individualist, while also having lower power distance and low uncertainty avoidance, and are located to the left of the map. They are also very masculine cultures, being located in the lower part of the map (a more negative projection over the axis indicates more masculinity).
Group 3: Group consisting of The Netherlands and Sweden. They are characterized by individualism (at the same time, they have less power distance and low uncertainty avoidance) and low masculinity (high femininity).
In either case, there have been not many cross-cultural research addressing specifically the differences between Spain and The Netherlands. Grande (
Power Distance Index (PDI) |
57 (41) | 38 (55) | 19 (14) |
Individualism (IDV) |
51 (28) | 80 (5) | −29 (23) |
Masculinity (MAS) |
42 (48) | 14 (67) | 28 (19) |
Uncertainty avoidance Index (UAI) |
86 (18) | 53 (48) | 33 (29) |
Long term orientation (LTO) |
48 (44) | 67 (22) | −19 (22) |
Indulgence vs. restraint (IVS) |
44 (46) | 68 (16) | −25 (30) |
Therefore, power distance is greater in Spain than in The Netherlands, meaning that Spanish people more readily accepts an unequal distribution of power. Also, this is the dimension with the least difference between both countries together with long term orientation, which are located near the midpoint (Figure
The Netherlands scores higher than Spain on individualism (a value of 80 compared to 51), taking fifth place. However, Spain is near the midpoint on the scale and is ranked 28th of all the countries in the study. The high level of individualism of The Netherlands is an indicator that this society has more individualist attitudes and fewer cohesive bonds with others. Dutch people has more self-confidence and is more independent. In the case of Spain, it may be said that relationships are closer, people have stronger bonds with other members of society, and there is greater group cohesiveness.
The score for masculinity is higher in Spain than in The Netherlands (42 compared to 14), although both countries are below the average score of 50. Therefore, we observe that Spain is already moving on from its formerly masculine character and is gradually acquiring feminine characteristics (Grande,
Regarding uncertainty avoidance, we observe that Spain scores close to the top of the uncertainty avoidance scale (a value of 86), ranking 19th, while The Netherlands scores close to the midpoint of the range (a value of 53) and ranks 48th. It should also be remarked that this is the dimension with the higher difference between both countries. Thus, we can state that the higher uncertainty avoidance showed by Spanish people means a higher willingness adopt regulations and laws which are intended to minimize uncertainty levels. They attempt to control everything in order to avoid the unexpected. As a result of high uncertainty avoidance, the society is more resistant to change (Hofstede,
With reference to the long term orientation dimension, Spain scores lower than The Netherlands (48 vs. 67) meaning that Spanish culture tends to maintain traditions and norms and more reluctant to social changes. By contrary, Dutch culture has a more pragmatic orientation with easily adaptable traditions to changing conditions, strong propensity to save and invest and a high perseverance in achieving objectives.
Finally, the indulgence score for Spain is lower than for The Netherlands (44 vs. 68). These results mean that Spain can be defined as a restrained society with tendency to pessimism and where people think that their actions are restrained by social norms. The Netherlands is a clear example of indulgence, with a higher willingness to realize their needs and desires regarding enjoying life and having fun. They are more optimistic and place a higher importance on leisure time.
From another point of view, Fischer et al. (
This research was based on an online survey to SNS users in Spain and The Netherlands that was carried out in 2009–2010 to people with ages between 16 and 74. For both samples, participants who had account and used the SNS frequently were chosen to be analyzed in. The final sample size was of 799 SNS users (399 from Spain and 400 from The Netherlands), and was selected using a non-probabilistic quota sampling method.
This study was carried out in accordance with the current legal and ethical recommendations about privacy of personal data. During the whole process the ICC/ESOMAR International Code on Market and Social Research practices and norms were also taken into account. Specifically, questions regarding handling personal data and personal identifiers and other privacy policy issues recommendations included in the ICC/ESOMAR code.
In this method, a population is first segmented into mutually exclusive sub-groups; then, the number of sampled units in each category is specified, to assure that all the sub-groups are adequately represented in the sample (Lim and Ting,
To develop the segmentation and profiling of the SNS users a latent segmentation methodology, using the Latent Gold 4.5 statistical software was used. This methodological approach allows the assignation of individuals to the segments based on their probability of belonging to the different clusters, breaking with the restrictions of deterministic assignment inherent to non-hierarchical cluster analysis (Dillon and Kumar,
Traditional clustering methods, like K-means, have been reported to be useful in the social sciences. Nevertheless, it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous (Bolin et al.,
The variables that we used as indicators for the cluster analysis were based on the frequency with which users engage in different activities within the SNS on a four point scale (i.e., never, rarely, sometimes or frequently), to avoid the mid-point and force to form an opinion. On the other hand, different social-demographic characteristics (i.e., gender and age) were introduced as covariates to profile the resulting segments. Other covariates were experience with SNS, frequency of participation, time spent browsing in SNS, location, number and type of contacts, number of SNS used, and main motives for using these websites. Based on the answers of the different individuals, with regard to these questions, we obtained different grouping patterns that fulfill the principles of maximum internal coherence and maximum external differentiation (see Table
Once the latent segmentation approach was applied, the first step consisted of selecting the optimum number of segments. Latent Gold estimates models from one (i.e., no heterogeneity) up to eight (i.e., maximum heterogeneity). Tables
1-Cluster | −9957.2188 | 20273.7752 | 60 | 19914.4376 | 0.0000 | 1 | 1 |
2-Cluster | −8691.7527 | 18024.3243 | 107 | 17383.5054 | 0.0132 | 0.95 | 0.96 |
3-Cluster | −8332.3777 | 17587.0555 | 154 | 16664.7554 | 0.0210 | 0.94 | 0.95 |
− |
|||||||
5-Cluster | −7951.6918 | 17388.6460 | 248 | 15903.3836 | 0.0317 | 0.94 | 0.93 |
6-Cluster | −7828.8586 | 17424.4607 | 295 | 15657.7171 | 0.0421 | 0.93 | 0.92 |
7-Cluster | −7717.7561 | 17483.7370 | 342 | 15435.5122 | 0.0286 | 0.95 | 0.94 |
8-Cluster | −7663.3435 | 17656.3930 | 389 | 15326.6870 | 0.0256 | 0.96 | 0.95 |
1-Cluster | −8388.3321 | 17136.1520 | 60 | 16776.6642 | 0.0000 | 1 | 1 |
2-Cluster | −7483.5478 | 15614.1738 | 108 | 14967.0956 | 0.0182 | 0.93 | 0.94 |
− |
|||||||
4-Cluster | −7114.0080 | 15450.2748 | 204 | 14228.0160 | 0.0406 | 0.91 | 0.91 |
5-Cluster | −7022.5604 | 15554.9699 | 252 | 14045.1209 | 0.0489 | 0.91 | 0.90 |
6-Cluster | −6923.1160 | 15643.6714 | 300 | 13846.2321 | 0.0444 | 0.93 | 0.92 |
7-Cluster | −6845.0906 | 15775.2109 | 348 | 13690.1812 | 0.0261 | 0.96 | 0.95 |
8-Cluster | −6780.0176 | 15932.6552 | 396 | 13560.0352 | 0.0266 | 0.96 | 0.95 |
The fit of the model was evaluated with the Bayesian Information Criterion (BIC), which allows the identification of the model with the least number of classes that best fits to the data. BIC statistic has been used to select the optimum number of segments, since it is especially useful in comparing models (Magidson and Vermunt,
Based on the BIC equation
This statistic (information criteria) weight fit and parsimony by adjusting the LL to account for the number of parameters in the model. The lowest BIC value was considered as the best model indicator (Vermunt and Magidson,
In the case of Spain sample, the best alternative was reflected in a final solution of four different user groups. In the case of The Netherlands sample, the BIC is minimized in three groups.
The Model Fit likelihood ratio chi-squared statistic (L2) can be interpreted as “indicating the amount of the observed relationship between the variables that remains unexplained by a model; the larger the value, the poorer the model fits the data and the worse the observed relationships are described by the specified model” (Vermunt and Magidson,
In addition to that set forth in Tables
Size | 18.6% | 41.3% | 25.2% | 36.2% | 47.4% | 19.9% | 11.3% | ||||||
Never | 0.068 | 0.050 | 0.073 | 0.000 | 0.180 | 99.9 | 87.8 | <0.001 | <0.001 | 0.4 | 0.3 | ||
Rarely | 0.315 | 0.191 | 0.165 | 0.227 | 0.005 | 0.252 | |||||||
Sometimes | 0.220 | 0.174 | 0.427 | ||||||||||
Frequently | 0.022 | 0.211 | 0.249 | 0.122 | |||||||||
Never | 0.025 | 0.032 | 0.264 | 0.000 | 0.018 | 124.2 | 80.4 | <0.001 | <0.001 | 0.5 | 0.4 | ||
Rarely | 0.317 | 0.265 | 0.129 | 0.147 | 0.001 | 0.117 | |||||||
Sometimes | 0.206 | 0.071 | 0.328 | 0.121 | |||||||||
Frequently | 0.009 | 0.003 | 0.205 | 0.180 | 0.049 | 0.357 | |||||||
Never | 0.096 | 0.054 | 0.000 | 124.0 | 88.5 | <0.001 | <0.001 | 0.5 | 0.4 | ||||
Rarely | 0.266 | 0.208 | 0.160 | 0.000 | |||||||||
Sometimes | 0.154 | 0.066 | |||||||||||
Frequently | 0.008 | 0.137 | 0.197 | ||||||||||
Never | 0.281 | 0.200 | 0.039 | 0.232 | 74.5 | 59.4 | <0.001 | <0.001 | 0.3 | 0.3 | |||
Rarely | 0.217 | 0.089 | 0.291 | 0.263 | 0.266 | 0.119 | 0.285 | ||||||
Sometimes | 0.065 | 0.008 | 0.113 | 0.358 | |||||||||
Frequently | 0.009 | 0.000 | 0.134 | 0.197 | 0.018 | 0.146 | |||||||
Never | 0.430 | 0.284 | 0.069 | 0.027 | 0.042 | 0.002 | 0.002 | 97.9 | 80.1 | <0.001 | <0.001 | 0.4 | 0.3 |
Rarely | 0.370 | 0.252 | 0.370 | 0.058 | 0.089 | ||||||||
Sometimes | 0.113 | 0.167 | 0.452 | ||||||||||
Frequently | 0.004 | 0.004 | 0.086 | 0.169 | 0.065 | 0.330 | |||||||
Never | 0.268 | 0.209 | 0.018 | 0.022 | 0.028 | 0.000 | 0.004 | 98.6 | 64.9 | <0.001 | <0.001 | 0.4 | 0.2 |
Rarely | 0.337 | 0.317 | 0.113 | 0.127 | 0.135 | 0.003 | 0.043 | ||||||
Sometimes | 0.167 | 0.442 | |||||||||||
Frequently | 0.031 | 0.060 | 0.258 | 0.234 | 0.268 | ||||||||
Never | 0.098 | 0.067 | 0.284 | 0.000 | 0.126 | 121.1 | 64.3 | <0.001 | <0.001 | 0.5 | 0.2 | ||
Rarely | 0.276 | 0.227 | 0.207 | 0.176 | 0.262 | 0.001 | 0.195 | ||||||
Sometimes | 0.199 | 0.105 | 0.127 | ||||||||||
Frequently | 0.009 | 0.017 | 0.104 | 0.136 | 0.135 | 0.281 | |||||||
Never | 0.240 | 0.190 | 0.008 | 0.261 | 93.1 | 63.3 | <0.001 | <0.001 | 0.4 | 0.3 | |||
Rarely | 0.178 | 0.051 | 0.322 | 0.302 | 0.266 | 0.059 | |||||||
Sometimes | 0.030 | 0.001 | 0.058 | 0.324 | 0.236 | ||||||||
Frequently | 0.002 | 0.000 | 0.112 | 0.147 | 0.013 | 0.167 | |||||||
Never | 0.264 | 0.062 | 0.236 | 0.006 | 0.076 | 81.2 | 71.0 | <0.001 | <0.001 | 0.3 | 0.3 | ||
Rarely | 0.300 | 0.236 | 0.288 | 0.162 | 0.249 | 0.042 | 0.150 | ||||||
Sometimes | 0.261 | 0.108 | 0.343 | 0.368 | |||||||||
Frequently | 0.049 | 0.022 | 0.094 | 0.301 | 0.190 | ||||||||
Never | 0.179 | 0.169 | 0.219 | 83.1 | 63.1 | <0.001 | <0.001 | 0.3 | 0.3 | ||||
Rarely | 0.241 | 0.072 | 0.255 | 0.299 | |||||||||
Sometimes | 0.038 | 0.005 | 0.044 | 0.344 | 0.154 | 0.352 | 0.352 | ||||||
Frequently | 0.002 | 0.000 | 0.003 | 0.116 | 0.026 | 0.124 | 0.136 | ||||||
Never | 0.298 | 0.364 | 66.7 | 32.6 | <0.001 | <0.001 | 0.2 | 0.1 | |||||
Rarely | 0.128 | 0.065 | 0.202 | 0.141 | 0.185 | ||||||||
Sometimes | 0.011 | 0.015 | 0.032 | 0.276 | 0.083 | 0.227 | 0.215 | ||||||
Frequently | 0.001 | 0.001 | 0.001 | 0.055 | 0.019 | 0.037 | 0.094 | ||||||
Never | 0.215 | 0.142 | 0.052 | 0.111 | 0.012 | 67.9 | 48.1 | <0.001 | <0.001 | 0.2 | 0.2 | ||
Rarely | 0.242 | 0.301 | 0.302 | 0.243 | 0.172 | 0.220 | 0.078 | ||||||
Sometimes | 0.105 | 0.316 | |||||||||||
Frequently | 0.007 | 0.066 | 0.047 | 0.136 | 0.212 | 0.165 | 0.393 | ||||||
Never | 43.1 | 33.4 | <0.001 | <0.001 | 0.1 | 0.1 | |||||||
Rarely | 0.148 | 0.146 | 0.221 | 0.218 | |||||||||
Sometimes | 0.058 | 0.057 | 0.248 | 0.190 | |||||||||
Frequently | 0.010 | 0.009 | 0.121 | 0.072 | |||||||||
Never | 0.079 | 0.041 | 0.255 | 104.6 | 45.7 | <0.001 | <0.001 | 0.4 | 0.3 | ||||
Rarely | 0.216 | 0.057 | 0.339 | 0.236 | 0.275 | 0.172 | 0.275 | ||||||
Sometimes | 0.045 | 0.004 | 0.173 | 0.145 | |||||||||
Frequently | 0.000 | 0.000 | 0.016 | 0.189 | 0.035 | 0.276 | 0.161 | ||||||
Never | 0.181 | 0.019 | 0.082 | 95.2 | 74.0 | <0.001 | <0.001 | 0.5 | 0.4 | ||||
Rarely | 0.043 | 0.057 | 0.279 | 0.333 | 0.305 | 0.121 | 0.194 | ||||||
Sometimes | 0.000 | 0.003 | 0.079 | 0.180 | |||||||||
Frequently | 0.000 | 0.000 | 0.004 | 0.087 | 0.034 | 0.365 | 0.299 | ||||||
Never | 0.091 | 0.021 | 0.048 | 111.3 | 71.6 | <0.001 | <0.001 | 0.5 | 0.4 | ||||
Rarely | 0.125 | 0.116 | 0.294 | 0.251 | 0.295 | 0.123 | 0.152 | ||||||
Sometimes | 0.014 | 0.020 | 0.112 | 0.254 | |||||||||
Frequently | 0.000 | 0.000 | 0.005 | 0.127 | 0.017 | 0.291 | 0.192 | ||||||
Never | 0.065 | 0.314 | 0.004 | 0.036 | 116.0 | 79.0 | <0.001 | <0.001 | 0.6 | 0.4 | |||
Rarely | 0.178 | 0.118 | 0.294 | 0.231 | 0.058 | 0.168 | |||||||
Sometimes | 0.029 | 0.010 | 0.104 | 0.275 | |||||||||
Frequently | 0.000 | 0.000 | 0.003 | 0.115 | 0.043 | 0.391 | 0.304 | ||||||
Never | 0.173 | 0.269 | 0.023 | 0.026 | 88.9 | 88.9 | <0.001 | <0.001 | 0.5 | 0.4 | |||
Rarely | 0.017 | 0.179 | 0.266 | 0.322 | 0.284 | 0.131 | 0.100 | ||||||
Sometimes | 0.000 | 0.050 | 0.075 | ||||||||||
Frequently | 0.000 | 0.003 | 0.003 | 0.087 | 0.085 | 0.328 | 0.406 | ||||||
Never | 0.219 | 0.198 | 0.281 | 87.9 | 54.9 | <0.001 | <0.001 | 0.4 | 0.3 | ||||
Rarely | 0.077 | 0.031 | 0.053 | 0.226 | 0.265 | ||||||||
Sometimes | 0.003 | 0.001 | 0.002 | 0.318 | 0.104 | 0.336 | |||||||
Frequently | 0.000 | 0.000 | 0.000 | 0.072 | 0.014 | 0.082 | 0.120 | ||||||
Never | 0.350 | 0.390 | 38.9 | 30.7 | <0.001 | <0.001 | 0.3 | 0.3 | |||||
Rarely | 0.075 | 0.001 | 0.001 | 0.354 | 0.170 | ||||||||
Sometimes | 0.004 | 0.000 | 0.000 | 0.252 | 0.010 | 0.255 | 0.133 | ||||||
Frequently | 0.000 | 0.000 | 0.000 | 0.040 | 0.001 | 0.0409 | 0.062 |
Table
Gender | Men | 0.467 | 0.474 | 0.426 | 0.346 | 0.310 | 22.2 | 2.9 | 0.000 | 0.234 | ||
Women | 0.353 | 0.393 | ||||||||||
Age | Less than 25 | 0.117 | 0.162 | 0.162 | 0.180 | 74.5 | 20.8 | 0.000 | 0.008 | |||
From 25 to 29 | 0.091 | 0.102 | 0.227 | 0.169 | 0.148 | 0.273 | 0.068 | |||||
From 30 to 35 | 0.104 | 0.168 | 0.144 | 0.182 | 0.159 | |||||||
From 36 to 44 | 0.221 | 0.192 | 0.227 | 0.185 | 0.091 | 0.250 | ||||||
Over 44 | 0.124 | 0.209 | 0.238 | 0.052 | 0.182 | |||||||
Experience with SNS | Less than 1 month | 0.039 | 0.036 | 0.035 | 0.008 | 0.045 | 35.1 | 10.0 | 0.000 | 0.124 | ||
Between 1 and 6 mo. | 0.314 | 0.109 | 0.277 | 0.094 | 0.136 | 0.047 | ||||||
Between 6 mo. and 1 year | 0.196 | 0.137 | 0.269 | 0.252 | 0.071 | 0.276 | 0.200 | |||||
Over 1 year | 0.421 | 0.710 | 0.355 | |||||||||
Frequency of Participation in SNS | At least once a week | 0.109 | 0.091 | 0.252 | 0.000 | 0.135 | 323.4 | 114.0 | 0.000 | 0.000 | ||
Several times a week | 0.095 | 0.162 | 0.334 | 0.070 | 0.212 | |||||||
At least once a day | 0.019 | 0.137 | 0.194 | 0.299 | 0.293 | |||||||
Several times a day | 0.000 | 0.035 | 0.288 | 0.245 | 0.129 | 0.281 | ||||||
Time SNS used weekly |
Less than 1 h | 0.241 | 0.281 | 0.372 | 0.103 | 0.116 | 195.4 | 143.3 | 0.000 | 0.000 | ||
Between 1 and 5 h | 0.247 | 0.248 | 0.353 | 0.453 | ||||||||
More than 5 h | 0.000 | 0.006 | 0.220 | 0.205 | 0.098 | |||||||
Profile location in the SNS | Public | 0.078 | 0.267 | 0.222 | 0.285 | 0.227 | 63.3 | 18.0 | 0.000 | 0.006 | ||
Some private and others public | 0.111 | 0.129 | 0.262 | 0.348 | 0.284 | |||||||
Private | 0.317 | 0.504 | 0.237 | 0.425 | ||||||||
Does not know | 0.177 | 0.139 | 0.077 | 0.063 | 0.090 | 0.000 | 0.000 | |||||
Number of contacts | Less than 10 | 0.246 | 0.124 | 0.140 | 0.139 | 0.038 | 0.066 | 125.8 | 25.3 | 0.000 | 0.000 | |
From 10 to 50 | 0.495 | 0.485 | 0.257 | 0.373 | 0.224 | |||||||
From 51 to 100 | 0.093 | 0.216 | 0.225 | 0.247 | 0.294 | 0.249 | 0.234 | |||||
More than 100 | 0.020 | 0.197 | 0.166 | 0.117 | ||||||||
Nature of contacts |
People known outside of Internet | 0.379 | 0.784 | 0.750 | 0.798 | 55.6 | 17.4 | 0.000 | 0.000 | |||
People known before but now only have contact through Internet | 0.243 | 0.730 | 0.609 | 88.8 | 5.4 | 0.000 | 0.067 | |||||
People known through Internet with possibility of having real contact | 0.051 | 0.046 | 0.108 | 0.166 | 0.244 | 0.152 | 80.6 | 12.7 | 0.000 | 0.002 | ||
People known on the Internet with no possibility of real contact | 0.026 | 0.117 | 0.097 | 0.235 | 0.229 | 0.178 | 78.2 | 7.7 | 0.000 | 0.022 | ||
Number of SNS they use | 1 | 0.009 | 0.062 | 0.503 | 0.025 | 0.523 | 158.9 | 24.3 | 0.000 | 0.018 | ||
2 | 0.331 | 0.233 | 0.422 | 0.364 | 0.204 | |||||||
3 | 0.169 | 0.084 | 0.225 | 0.127 | 0.276 | |||||||
More than 3 | 0.015 | 0.024 | 0.165 | 0.226 | 0.117 | 0.068 | ||||||
Motives for using SNS | Entertainment | 0.429 | 0.360 | 0.806 | 0.802 | 69.0 | 23.0 | 0.000 | 0.000 | |||
Professional interest | 0.163 | 0.099 | 0.174 | 0.148 | 5.2 | 5.9 | 0.160 | 0.052 | ||||
Because was invited | 0.579 | 0.461 | 0.576 | 0.587 | 6.9 | 0.8 | 0.076 | 0.667 | ||||
Novelty | 0.141 | 0.174 | 0.155 | 0.167 | 0.286 | 18.0 | 11.2 | 0.000 | 0.004 | |||
Maintain contact with friends | 0.329 | 0.496 | 0.734 | 0.635 | 68.7 | 24.4 | 0.000 | 0.000 | ||||
Because their friends were there | 0.052 | 0.159 | 0.284 | 0.187 | 0.354 | 51.3 | 35.6 | 0.000 | 0.000 | |||
Keep informed about events, parties, etc. | 0.027 | 0.027 | 0.157 | 0.146 | 0.060 | 112.6 | 33.3 | 0.000 | 0.000 | |||
Keep informed of comments on new products | 0.079 | 0.035 | 0.011 | 0.103 | 0.085 | 20.5 | 5.0 | 0.000 | 0.081 | |||
Make new friends | 0.043 | 0.047 | 0.165 | 0.276 | 0.176 | 38.6 | 45.9 | 0.000 | 0.000 | |||
Make contacts on a professional level | 0.096 | 0.136 | 0.048 | 0.163 | 0.175 | 23.1 | 6.2 | 0.000 | 0.044 | |||
Know better or have a closer relationship with persons with whom they do not have a direct relationship | 0.027 | 0.060 | 0.209 | 0.174 | 0.114 | 86.2 | 8.7 | 0.000 | 0.013 | |||
Look for partner/dating | 0.027 | 0.028 | 0.075 | 0.049 | 0.089 | 4.0 | 1.5 | 0.257 | 0.477 |
Table
Tests associated with chi-square statistic (χ2) conclude that significant differences exist between the segments for each country regarding gender, age, frequency of participation in SNS, weekly time spent on SNS, profile location on these sites, number of contacts, types of contacts maintained (in The Netherlands there are no significant differences with regard to people previously known but now only in contact through the Internet), and number of SNS in which the user has an account and is active. With regard to experience in SNS, there are only significant differences between the segments in Spain. For most of the motives that trigger the use of the SNS we also found significant differences in both countries, except for reasons such as the use of these sites for professional purposes, because the user was invited, and in the case of looking for a partner or for dating.
The main characteristics of the above-mentioned groups in both countries, listed from a lesser to a higher intensity of SNS use, are detailed below:
Introvert User. This is the smallest group in Spain, representing 18.6% of SNS users, but in The Netherlands this group is bigger (41.3%). This is the least active group, using SNS mainly to send private messages. In other words, they use these sites as an email substitute, as well as for updating their profile, which they do not do very often. Moreover, this group in The Netherlands uses SNS to look for friends. This segment is mainly made up by men in Spain and by women in The Netherlands, with a high percentage being over 44 years old. These users connect to SNS with low frequency and they spend less time on them. Spanish Introvert Users usually have a public profile and Dutch users have a private profile, having both fewer than 50 contacts. Introvert Users have contacts that they know outside the Internet and people with whom they have maintained previous contacts offline. The majority use only one SNS mostly because they were previously invited, and the Dutch users also use it to contact friends and acquaintances.
Novel User. This group is unique in Spain (25.2%). Once in a while they comment on their friends' photos, send private and public messages and make comments on what is said or done by the people who send them the pictures. They also update their profile, but with lower probability, they look for information of things of their interest, label friends in pictures, and browse through user profiles. The majority are women less than 29 years old. They participate several times a week in SNS, spending between 1 and 5 h weekly. The majority have a private profile, with less than 50 contacts that they have known outside of Internet and with whom they may or may not have physical contact at this moment. They have accounts in two SNS and use them mainly for entertainment, to keep in touch with friends and people they know and because they were invited.
Versatile User. The largest group in both countries (36.2% in Spain and 47.4% in The Netherlands). A Versatile User carries out almost all activities, although not all with a high intensity. The majority in both countries share or upload photos, update their profile and send private messages. This group of users is more active in Spain than in The Netherlands. The majority of Spanish Versatile Users, moreover, comment on friends' photos, comment on what the people they follow are saying, send public messages, share interesting website links and communicate ideas/reflections. These activities are also carried out by the Dutch Versatile Users, but not by all of them. On the other hand, once in a while these users in both countries obtain information about things that interest them, look for friends and inform about what they are doing at the time of writing. Moreover, only Spanish Versatile Users occasionally browse across SNS and their users' profile, label friends in pictures, communicate news or subjects that they believe can be of interest to all and share their state of mind, and rarely download applications or games, or inform about brands or products that they use. This segment is made up mainly of men in Spain and women in The Netherlands, between 30 and 44 years old. They participate several times a week in SNS sessions, spending between 1 and 5 h a day. Spanish Versatile Users are users of two or three SNS, with both private and public profiles, and they have between 10 and 100 contacts. However, Dutch Versatile Users are users of one SNS, with a private profile, and they have more than 50 contacts. The majority of contacts in both countries are people they knew before, in a physical environment. The majority use SNS for entertainment, to maintain contact with friends, and because they were invited.
Expert-Communicator User. This group covers 19.9% of the Spanish and 11.3% of Dutch SNS users. This is the most active user of all groups, in addition to having the greatest probability of carrying out activities similar to the Versatile User. They also comment on ads and publicity. Their most outstanding feature is that they have a high probability of frequently carrying out several activities, specifically sharing or uploading photos, sending private messages and obtaining information of their interest. Moreover, Spanish Expert-Communicator Users carry out other activities frequently, while Dutch Expert-Communicator Users do them less frequently: specifically, commenting on friends' photos, commenting on what the people they follow do/say, browsing across SNS and their users' profiles, updating their profile, sending public messages and labeling friends in pictures. The highest proportion in both countries is composed of women aged under 25, with quite a lot of experience in SNS. They are the most participative users within these sites. In Spain, this group is made up of users of more than two SNS, and in The Netherlands they are users of one SNS, although there are a high proportion of users with more than two accounts. Many of these users in both countries have a private profile, and a higher number of contacts than the other groups, mainly with people they know offline. They use SNS for entertainment, because they were invited and for keeping in touch with friends. Moreover, the majority of Spanish users uses SNS to keep informed about events, parties, etc., and the Dutch users because their friends were there. In addition, this group encompasses a greater proportion of individuals than the rest of the groups who use the sites because of their novelty, to keep informed of comments on new products that they are interested in, to make new friends and to know better or have a closer relationship with persons with whom they do not have a direct relationship. Also, in this group, there is a higher proportion than in other groups of Spanish users who use SNS to keep informed of comments on new products of their interest, and Dutch users looking for partners.
Social Media Networks provide people with bodies that are combinations of embodied and technologically mediated action. These tools create multiple visibility formats within the infospheres of social media. Goodings and Tucker (
The differences in the adoption and behaviors within SNS may be due both to personal characteristics and cultural differences. According to our theory review, there are strong motives to believe that there are connections between culture and the way people use specific information technologies (Straub et al.,
Hofstede's cultural framework has been strongly supported empirically (Søndergaard,
The analysis of these cultural differences can be useful both for academic and professional reasons. The comparative analysis of the profiles of users of this type of web app, are increasingly relevant to observe the homogeneity or heterogeneity of their online behavior and to guide e-marketing decisions by companies in countries with technologically different cultures (Kim et al.,
Applying latent segmentation, in Spain, we have obtained four different user segments which have been classified as “Introvert,” “Novel,” “Versatile,” and “Expert-communicator.” However, in The Netherlands, we obtained only three groups that we have tagged as “Introvert,” “Versatile,” and “Expert-communicator.” These three segments are very similar to those obtained in Spain. In fact, we have call them the same. The results indicate that the socio-demographic characteristics by themselves are not adequate segmentation criteria for this context. More attention should be paid to criteria related to the use of SNS. The study reveals the different behavior of these segments, providing organizations with important information as a basis for designing strategies for the use of SNS as marketing tools.
Each group uses different SNS applications and with different frequencies. The “Introvert” uses these sites once in a while to communicate with their friends privately. In sum, the “Novel” user, which only exists in Spain, uses the SNS once in a while to maintain contact with their friends (obtaining and giving information about each other). The “Versatile” user carries out different activities, although only occasionally. However, the “Expert-communicator” users carry out a greater number of activities and with greater frequency, especially relating to bi-directional communication with their friends. In both countries, the “Expert-Communicators” present the most interesting possibilities as potential sources of market information and possibilities for engagement as brand ambassadors.
As we proposed in the introduction section, companies should understand the heterogeneity in consumer behaviors within these virtual social environments. Segmentation present numerous benefits allowing a greater customer satisfaction and higher level of customer loyalty. Therefore, it might be interesting for companies to identify “Expert-Communicators” segments and to attempt to increase the number of engaged customers and create brand advocates by better understanding the needs and motives of their socially networked customers. These consumers could be engaged in open dialogue, since they present an interesting possibility as potential sources of marketing information and for engagement as brand ambassadors. However, it is worth noting that this segment is not the only one with interest for marketing purposes, it depends on the objectives and the target of the company.
The information available in SNS, voluntarily uploaded by their users, allows companies to obtain a huge amount of information about their customers regarding their habits, personalities, and lifestyles. This information allows refined market segmentation. An analysis of users' behaviors can also provide an early warning of unknown product problems as companies will have a high valuable information about the customer preferences, complains…and also can help to detect some possible problems in the beginning stages of the product introduction in the market. By monitoring the comments and the information available in SNS, companies will have a highly valuable information to improve the knowledge of their customer needs and preferences. Therefore, businesses can use SNS as a source of customer voice. They can obtain, at very low cost, the direct and valuable market information that they need to improve decision-making, to monitor opinions and complaints about the organization, or even for providing suggestions about new products or services.
The paper proposes an integrated and interesting segmentation methodology to understand affective and behavioral dimensions of SNS users and can be extended to the analysis of other tools or technological devices. In that way, and a as future research we propose, for example, to extend the cross-cultural analysis focussed on the use of SNS through the mobile technologies (Aldás et al.,
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research was supported by the Junta de Comunidades de Castilla-La Mancha (reference PII-2014-018-P), and by the Ministerio de Economía y Competitividad, Gobierno de España (reference ECO2014-55881).