Against the background of theoretical considerations and literature review, this thesis provides the findings of the preliminary investigation of Greek consumers on their adoption of the Internet of Things (IoT). The primary objective of the analysis was to explain the effect of various technology adoption factors in consumer behavioral intention of future use of IoT products. It was concluded that attitude towards IoT is mostly determined by personal innovativeness of individuals and secondly by perceived technological usability.
The success of IoT products depends on the target group targeting individuals with high perceived technological usefulness, high self-efficacy and high personal innovativeness. Such consumers are better able to express high expectations of IoT offerings and thereby increase their intention for future use.
Introduction
Research Objectives
Understanding user usability, convenience, and protection is a requirement for user adoption of IoT technology. Many reports have examined the technological problems associated with the application of IoT technology. Consequently, this research aims to bridge the gap regarding customer adoption of IoT service.
The goal of the analysis is therefore to recognize factors that influence the acceptance of IoT and to establish a model of factors that determine customer acceptance of IoT technology. In this analysis, the combined model is used to define factors influencing the reception of IoT services by customers in Greece.
Research Structure
Literature Review
- Internet of Things
- Industry 4.0
- Models of IoT adoption
- Drivers of IoT adoption
- Perceivedsecurity
- Demographics
- Network externalities
- Social networks
- Trust
- Perceived Ease
The number of IoT providers determines the supply side of the network, while the number of customers determines the demand side of the network. Perceived usefulness is another important direct network externality based on the number of subscribers. Consequently, indirect externalities arise on the supply side of the network (Lin & Bhattacherjee, 2008).
The assumed complementarity is therefore known as an indirect externality of the network in the present analysis. Gao and Bai (2014) studied the effect of the social impact on the adoption of IoT services.
Methodology
- Research Questions and Research Hypotheses
- Research Instrument
- Research Procedure and Sampling Issues
- Statistical and Econometric Techniques
All items refer to respondents' possible perceptions of the ease of use of technology in general, with 7 of them needing a reverse scale. The overall score of the ease of use of the technology in general is the result of the average of the answers of each respondent, of course after inverse scaling. Therefore, values close to "1" indicate low perceived ease of use of the technology in general, while values close to "5".
To measure self-efficacy, i.e. the competence to use technology in general, the related instrument includes 12 items with a Likert scale from 1 = disagree to 5 = strongly agree. All items refer to the possible respondents' perceived self-efficacy of using technology in general, of which 7 of them require reverse scaling. The overall score of self-efficacy of using technology in general results in the average of each respondent's answer, reverse-scaled of course.
Therefore, values close to '1' indicate low perceived self-efficacy regarding the competence to use technology in general, while values close to. In order to measure personal innovation in the domain of technology in general, the related instrument includes 4 items with a Likert scale from 1 = strongly disagree to 5 = strongly agree. All items refer to the possible respondents' perceived personal innovativeness in the domain of technology in general, requiring one of them to be inversely scaled.
The overall score of personal innovation in the domain of technology in general leads to the average of each respondent's answer, of course to reverse scale. Therefore, values close to '1' indicate low perceived personal innovativeness in the domain of technology in general, while values close to '5' indicate high perceived personal innovativeness in the domain of technology in general. EFFICACYi = perceived self-efficacy of technology in general of i-participant INNOVi = perceived personal innovativeness in the domain of technology of i-.
Results
Sample Demographics
Therefore, almost half of the sample consists of men and the other half of women. Therefore, almost half of the sample consists of middle-aged participants and the other half consists of younger or older participants. Regarding the participants' education, 15 participants had a high school education. Participants have a BSc degree or university title (43.6%) and the remaining 109 participants have an MSc or even a PhD degree (49.6%).
Regarding the marital status of the participants, 89 participants are single, including cases of divorced or widowed (40.5%) and the remaining 131 participants are currently married (59.5%). More than half of the sample therefore consists of married participants who live in their own families. In terms of the participants' profession, 135 participants are currently working as private clerks. The participants currently work as public clerks. The participants are currently entrepreneurs or self-employed (14.5%) and the remaining 19 participants are unemployed or studying without work (8.6%).
Regarding participants' awareness of the Internet of Things (IoT), there are 93 participants who currently have and use at least one gadget of IoT belonging to the USE category (42.3%). There are 47 participants who are curious about what a gadget of IoT can do for them and have considered getting one belonging to the KNO category (21.4%). There are 7 participants who have looked into getting at least one IoT gadget but have decided to belong to it.
Finally, there are 73 participants who know little or nothing about IoT gadgets and their associated functions that belong to the NOK category. More than half of the sample therefore consists of participants who are aware of IoT gadgets. Moreover, most participants are aware of IoT and a significant portion of the sample is currently using these gadgets.
Scales Reliability
It is not surprising that there is not a single participant who has had at least one IoT device in the past but no longer uses one that belongs to the DIS category, in a sense that such devices are relatively new in the technology market. Moreover, conscious participants usually like such devices and are currently using or considering purchasing them. In summary, the sample is somewhat balanced in terms of gender, consists mostly of middle-aged participants, with a very high educational level and their own family, who mostly work as clerks.
The IoT scale consists of 4 items, the reliability of which is very high (α=0.886), well exceeding the corresponding threshold. The attitude towards IoT scale consists of 3 items whose reliability is very high (α=0.853) exceeding the corresponding threshold. Finally, the Future Use of IoT scale consists of 4 items whose reliability is very high (α=0.897) exceeding the relevant threshold.
In summary, all scales are characterized by very high reliability, as all Cronbach α coefficients far exceed the threshold of 0.7.
Scales Descriptive Statistics
Regarding Pearson correlation coefficients, they are all, for each pair of scales, statistically significant at 1% level (p<0.01) and positive, indicating a positive correlation between them. More specifically, correlations between performance expectation of IoT and usability, ease of use, self-efficacy and personal innovativeness are all positive, statistically significant and medium to high which at first glance confirms the research hypothesis H1.1. Correlations between attitudes towards IoT and usability, ease of use, self-efficacy and personal innovativeness are all positive, statistically significant and medium to high, confirming at first glance the research hypothesis H1.2.
The correlations between future use of IoT and performance expectation of IoT and attitude towards IoT are all positive, statistically significant and strong enough and prima facie confirm the research hypotheses H2.1 and H2.2.
Econometric Results Concerning Research Hypotheses
- Results for Research Hypothesis H1
- Results for Research Hypothesis H2
- Results for Research Hypothesis H3
Therefore, aware of IoT participants with higher (lower) perceived technological ease of use do not tend to express higher (lower) performance expectation of IoT devices. Therefore, aware IoT participants with higher (lower) expressed self-efficacy tend to express higher (lower) performance expectation of IoT devices. Therefore, aware IoT participants with higher (lower) expressed personal innovativeness tend to express higher (lower) performance expectation of IoT devices.
Therefore, unaware of IoT participants with higher (lower) perceived usefulness of the technology tend to express higher (lower) performance expectations from IoT devices. Therefore, unaware of IoT participants with higher (lower) technology ease of use, do not tend to express higher (lower) performance from IoT devices. Therefore, unaware of IoT participants with higher (lower) personal innovativeness tend to express higher (lower) performance expectations from IoT devices.
Therefore, IoT-aware participants with higher (lower) perceived usefulness of the technology tend to express higher (lower) attitudes toward IoT gadgets. Therefore, IoT-aware participants with higher (lower) expressed self-efficacy do not typically express higher (lower) performance expectations from IoT gadgets. Therefore, IoT-unaware participants with higher (lower) expressed self-efficacy tend to express higher (lower) expected performance from IoT gadgets.
Therefore, non-aware of IoT participants with higher (lower) expressed personal innovation ability do not tend to express higher (lower) performance expectation from IoT gadgets. Therefore, mindful IoT participants with higher (lower) expressed performance expectation from IoT tend to express higher (lower) future use of IoT gadgets. Therefore, aware of IoT participants with higher (lower) expressed positive attitude toward IoT tended to express higher (lower) future use of IoT gadgets.
Therefore, unaware IoT participants with higher (lower) performance expectation from IoT tend to express higher (lower) future use of IoT gadgets. Therefore, IoT unaware participants with higher (lower) positive attitudes toward IoT tend to express higher (lower) future use of IoT gadgets.
Conclusions
- Summary
- Discussion
- Implication of Findings
- Proposal for Future Research
Regarding research hypothesis H3.2, it is confirmed that attitude towards IoT positive effect on future use of IoT becomes stronger in the case that there is awareness of IoT, while it is not confirmed for performance expectation of IoT. On the other hand, even for individuals who are aware of IoT positive effect of usability on attitude towards IoT remains quite identical. More specifically, attitude toward IoT has a stronger positive effect on intention for future use of IoT for individuals who know about these gadgets.
Reflections on trust in devices: An informal investigation of human trust in an Internet-of-Things context. Vision and Challenges to Realize the Internet of Things: CERP-IoT - Cluster of European Research Projects on the Internet of Things, European Commission - Directorate-General for Information Society and Media, Brussels. The title of the thesis is "The Adoption of Internet of Things (IoT) by Greek consumers".
Choose the ONE statement that best describes your use and knowledge of the Internet of Things (IoT) and its capabilities. The next set of questions relates to your perception of your performance expectation in the Internet of Things space. The next set of questions concerns your perception about your attitude towards the Internet of Things.
The next set of questions is about your perception of your future use of the Internet of Things.
