The use of smart devices has increased greatly in the last ten years with users reaching out to the possibility to do more with them especially in the networking front. In this context there is a need to understand the connection between users' social demographic factors, their way to related to their smart devices, and the increasing adoption smart technologies in the industry 4.0 domains. This study was designed to evaluate the senso of belonging of a community using smart devices. In this way we evaluate intangible benefits that employees may gain from a more immerse relationship with their devices. We used a dataset of 586 anonymous respondent of an existing survey designed for capturing the relationships that humans develop with their smart devices. In particular, we investigate the relationships with smart device and particular background variables of the respondents using a chi-square test. The study showed that there is a significant relationship between users' sex and smart device type and their dependency on smart device. Both male and females, tends to think that smart device enables them to connect with a larger community and smart phone users connect more to large community than other smart devices users. This study provided several significant findings that strengthen the previous literature works on subject matter. Certain socio demographics variables like age, sex and smart device type showed a correlation between smart device users and their tendency to stay in touch with a larger community via their smart device.
of the relationship between human and devices will foster a more collaborative enjoyable and productive interaction.
A smart device, as the name implies, is a piece of electronic equipment that can communicate,
share, and collaborate with the user and other similarly equipped gadgets over the internet. Also
described as interactive electronic gadgets that respond to human directions and assist with everyday
tasks. Smartphones, tablets, phablets, smartwatches, smart glasses, and other personal electronics are
some of the most regularly used smart gadgets. The capacity to connect to a network and exchange
and interact remotely is what defines many smart gadgets, which are tiny, portable personal
electronics. For this reason, many television sets and freezers may be classified as "smart gadgets."
Smart devices are also important for social relationships and contribute to the way we relate among
individuals. For example, if you want to be sociable, you don't have to go out with my friends every
day or phone them every two hours. You can stay in touch with them using smart devices that are
directly connected with social media sites. While it is true that smartphones have made it easier to
maintain interpersonal relationships and perform duties [
We consequently advance the following hypothesis to be investigated: Hypothesis: My smart device enables me to be a part of a larger community.
The need to interact with other persons is an insatiable quest for all humans. The use of smart
devices has tried to satisfy such. While most persons long to connect to a larger community for
various needs like social networking, religious event, academic conferences, and others, we know that
the use of smart devices has made the easier to connect others seamlessly. Most smart devices have
different communication technologies like Bluetooth, WiFi, MMS, SMS, GPRS, GSM, 3G, 4G etc. to
enable users connect to multiple devices and multiple users at the same time [
In this study, we look at the relationship between users’ socio demographics variables and their ability to connect to a larger community using their smart device.
The rest of the paper is organized as follows. Section 2 reviews related works, outlining their similarities and differences. Section 3 comprises of the method used in this paper as well as a brief overview of the data. Section 4 consists of the results obtained in this research analysis. Section 5 concludes the paper with a brief summary and discuss the future work.
Barber and Santuzzi [
Research carried out in Australia investigated the smartphone usage and a need to stay connected
on among the adult [
Winskel and his team performed an experiment on the use of smartphone and study behavior
among students in Korea and Australia [
We intended to investigate how the use of smart device influence the user’s tendency to connect to a larger community. Firstly, we observed the association between each demographic variable (Age, Sex, Level of education, Type of education, Device Category) and users’ connection tendency. Consequently, we performed association analysis between each demographic variable and smart device dependance parameters. We employed the Pearson Chi-square test of independence. This statistical approach fits our data set that met the Chi-square test, in particular: • The simple random sampling was our chosen sampling method • All responses are in the same sample set. • All examined variables are categorical • The categories are mutually exclusive and • The expected frequency count for all category variables was 5 or higher.
In cases, where more than 20% of cells in a contingency table have expected frequency of less than 5, we employed the Fisher’s exact test to access whether there is a significant association between two categorical variables in the contingency table. In this research work as in most academic works, the significance factor or alpha value of 0.05 is chosen. We measure the strength of the association between the variables with the use of Cramer’s V test. The data have been analyzed using the Pandas package available in Python. 3.1.
The dataset used in the study is a report of responses to a survey designed to investigate the
relationship between people and their smart devices [
The survey captured the respondents’ demographic parameters which include: (i) age-group, (ii) sex, (iii) education level, (iv) education type, (v) work experience, (vii) country of residence, (viii) county of origin. The dataset was collected in May-July 2020 and is a sample of over 500 participants of various cultural allegiance and upbringing.
In this Section we present our observations in this study. The sample (N = 574) consist of 394 (68.64%) of male, 174 (30.31%) of female and 6 (1.04%) who prefer not to say. One can dig deep to know more about the sociodemographic characteristics of the participants by consulting this paper [22]. We investigate the relationship between each demographic variable and large community connection (My smart device enables me to be a part of a larger community).
First, we examine the association between age group and large community connection. Null Hypothesis (H1-0): There is no significant association between the two categorical variables – age group and large community connection. Alternative Hypothesis (H1-1): There is a significant association between the two categorical variables – age group and large community connection.
Second, we examine the relationship between sex and large community connection. Null Hypothesis (H2-0): There is no significant association between the two categorical variables –sex and large community connection. Alternative Hypothesis (H2-1): There is a significant association between the two categorical variables – sex and large community connection. With a p-value of 0.0197, there is strong evidence to reject the null hypothesis which shows there is a relationship between sex and large community connection with a weak association (Cramer’s Value =0.1436).
Next, we observed that 68% of these respondents lies in the “PhD” and “Professor” hence we created two subsets which holds “PhD” and “Professor” and “Others”. After which we created the hypothesis to examine the connection between education level and large community connection. Null
hypothesis (H3-0): There is no significant association between the two categorical variables – education level and large community connection. Alternative hypothesis (H3-1): There is a significant association between the two categorical variables – education level and large community connection. With a p-value of 0.3551, there is no indication to show there is a relationship between sex and large community connection hence the null hypothesis is accepted.
Next, we observed that 58% of these respondents belong to the Informatics, Computer science & Computer (ICC) group hence we created two subsets to manage this distribution with one subset for only respondents in ICC and the other for other groups. We created two hypotheses to evaluate the association between these variables – education type and large community connection. Null hypothesis (H4-0): There is no significant association between the two variables. Alternative hypothesis (H4-1): There is a significant association between the two variables. There was strong evidence to accept the null hypothesis with a p-value of 0.24
Finally, we accessed the relationship between device category and large community connection. We created two subsets for the device category. The first subset is for computers, smartphones and smart watches/bracelets and the second subset for other smart devices. Null hypothesis (H5-0): There is no significant association between the two variables (device category and the user tendency to connect to large community). Alternative hypothesis (H5-1): There is a significant association between the two variables. We accept the alternative hypothesis based on the result of the chi-square test that produced a p-value of 0.001. The association that exists between the variables is weak given that the Cramer’s value is 0.18. Table 1 summarizes the findings.
ability to connect with a large community
ability to connect with a large community
and user’s ability to connect with a large community
and user’s ability to connect with a large community
As further refinement of the Hypothesis 1: The respondent’s age, education level and type doesn’t play a significant role in user's smart device dependency. Respondent’s sex and device category played a significant role however their relationship is weak.
This study provided few significant findings that complement the current literature in smart devices, Industry 4.0 and HCI. In particular, previous studies focused on the smart phone use dependency and its impacts to the user’s work ethics [17, 18] as well as academic performance [19, 20]. This paper entails the use of smart device (including smart phones) and the need for the users to connect with a large community. Therefore, suggests that a proper understanding of how humans relate with their smart devices may play a significant role in understanding how to design a human centric production line. Findings indicate that there is a relationship between the need to connect with a large community and user’s sex. This confirms previous studies carried out recently [21, 22]. The user’s education type and education level doesn’t play a significant role for the user’s need to socialize with other. To complement this study, we intend to take into account potential data imbalances, and we would like to examine the user’s segmentation on their tendency to use their smart device to connect a large community.
We also intend to enrich this study by using AI based techniques to devise possible models that predict the user behavior and their contextual reactions. In particular we plan to use machine-learning algorithms like logistics regression, decision tree classifier, in order to combine all independent variables and see how they model the user’s behavior. We expect that the particular class of device plays a significant role in user’s ability to connect with a large community especially those with smartphone, smart wristwatches and brackets.
[17] J. Billieux, A. Schimmenti, Y. Khazaal, P. Maurage, A. Heeren, Are we over pathologizing everyday life? A tenable blueprint for behavioral addiction research, Journal of Behavioral Addictions 4 (2015) 119-123. doi: 10.1556/2006.4.2015.009. [18] D. Derks, A. B. Bakker, M. Gorgievski, Private smartphone use during worktime: A diary study on the unexplored costs of integrating the work and family domains, Computers in Human Behavior 114 (2021) 106530. doi: 10.1016/j.chb.2020.106530. [19] F. D. Alosaimi, H. Alyahya, H. Alshahwan, N. Al Mahyijari, S. A. Shaik, Smartphone addiction among university students in Riyadh, Saudi Arabia, Saudi Medical Journal 37 (2016) 675-683. doi: 10.15537/Smj.2016.6.14430. [20] Y. F. Chen, S. S. Peng, University students’ Internet use and its relationships with academic performance, interpersonal relationships, psychosocial adjustment and selfevaluation, CyberPsychology & Behavior 11 (2008) 467-469. doi: 10.1089/cpb.2007.0128. [21] E. Gentina, F. Rowe, Effects of materialism on problematic smartphone dependency among adolescents: The role of gender and gratifications, International Journal of Information Management 54 (2020) 102134. doi: 10.1016/j.ijinfomgt.2020.102134. [22] C. Chen, K. Z.K. Zhang, X. Gong, S. J. Zhao, M. K.O. Lee, L. Liang. Examining the effects of motives and gender differences on smartphone addiction, Computers in Human Behavior 75 (2017) 891-902. doi: 10.1016/j.chb.2017.07.002.check.