Motivating people to adopt electricity management system based on push–pull

Abstract

With growing concerns about power and environment issues, smart grid technology, such as meter data management systems, can be viewed as a great solution to handle issues of the environment and energy efficiency. Based on the push–pull–mooring (PPM) model, this research views dissatisfaction, personal innovativeness, information quality, locatability, economic benefits, and perceived value as predictors to examine their impact on switch intention to an electricity management system. The analysis results display that dissatisfaction has no significant effect on switch intention. As a mooring variable, personal innovativeness not only influences switch intention positively, but also moderates the relationship between perceived value and switch intention. Moreover, through perceived value, locatability and economic benefits influence switch intention positively. This research fills the gap in government policies as well as energy companies and provides ponderable suggestions if a government and energy companies would like to change citizens’ behavior from paper-based bill to electricity management system. The government and energy companies should not overlook the effect of personal innovativeness and perceived value and put more effort to publicize the advantages of locatability and economic benefits.

Keywords

Electricity management system smart grid pull–push–mooring model perceived value switch intention

Introduction

With the growing concerns on pollution, climate change, and electricity supply, companies and governments around the world are putting greater efforts into finding more efficient solutions. Smart grid technology, such as meters, data collectors, communications systems, and meter data management systems, can be viewed as a great answer to deal with issues of the environment and energy efficiency.¹ Peters et al.² regarded that using a smart grid can assist fossil fuel regions in adopting renewable energy. Collecting data from meters can help citizens conserve electricity and shift demand,³ as well as dynamic pricing.⁴

Despite the important role of smart grids, many studies just focus on the advanced metering infrastructure (AMI). For instance, Shirani et al.⁵ presented the topic of smart meters and examined the effect of smart technology among vulnerable consumers. Taso et al.⁶ adopted the theory of reasoned action (TRA) and the norm activation model (NAM) to explore people's intention to use a smart meter. Alkawsi et al.¹ also proposed that such experiences moderate the effects of effort expectancy, social influence, eco-effective feedback, habit, and privacy concerns on the behavior intention toward smart meters. Without fully utilizing electricity information collected from AMI, the smart grid might fail to reach its full potential. Therefore, instead of AMI, we need to fill the gap in the smart grid literature and focus more attention on smart data management systems.

Energy savings and efficiency are the advantages of using meter data management systems, but all the potential benefits of electricity management systems will only be understood if consumers adopt them at a greater scale. In their study of a smart grid roadmap, Honebein et al.⁷ also regarded that smart grids would fail to reach full potential without the involvement of customers. Follow-up research thus should conduct a more fundamental analysis of smart grids to understand customer experiences. Consequently, knowing consumers’ beliefs would be very helpful to unfold the key antecedences that affect their intention toward using an electricity management system. However, there is a shortage of research on the reasons driving customer engagements in smart grids.

The aim of this research is to fill this gap in the energy, policy, and electricity arenas and to recognize how to motivate people to adopt electricity management systems. Based on the push–pull–mooring (PPM) theory, this study shall examine how dissatisfaction, personal innovativeness, locatability, information quality, economic benefits, and perceived value influence peoples’ switch intention from paper-based billing to electricity management system usage. We also shall examine the moderating role of the mooring effect. In other words, this study explores the moderating effect of personal innovativeness among dissatisfaction, perceived value, and switch intention. Figure 1 illustrates the research framework.

Figure 1.

Research framework.

The rest of this research runs as follows. Based on the PPM theory, we develop our research framework and propose the hypotheses on dissatisfaction, personal innovativeness, locatability, information quality, economic benefits, perceived value, and switch intention. Next, we introduce the procedure of data collection and the measurement of each variable. The fourth section presents an analysis of reliability and validity, confirmatory factor analysis, as well as path analysis. Finally, this study proposes a conclusion, provides contributions for the real world and academia, and offers limitations and directions for future research.

Literature review

The push–pull–mooring theory

The push–pull–mooring (PPM) model is a classical theory in migration research.⁸ As a pioneer, Ravenstein⁹ proposed the push and pull effect as the foundation for demographic studies and argued that migration behavior is influenced by push and pull behaviors. The push effect means the negative reasons that make people leave their original place, including a lack of opportunities for personal development, bad environment, and natural disasters; the pull effect refers to the positive reasons that draw people to it, such as comfortable environment, better education opportunities, and higher salary works.¹⁰ In addition, some studies have proposed that PPM should include the mooring effect in order to solve the problem that the original PPM model is unable to totally explain the role of individual determinants in human migration behavior.⁸ The mooring effect often denotes personal or social factors that affect migration behavior,^11,12 such as personal characteristics and switch cost.¹³

The switch behavior of consumers between different service providers is similar to citizens who migrate between different physical places,^14,15 and the PPM model has been adopted to examine such behavior.^13,16 Thus, this study views PPM as our theoretical foundation and assumes that one variable, dissatisfaction, spurs users to abandon their paper-based electricity bill. One mooring factor includes personal innovativeness. Furthermore, we include locatability, information quality, economic benefits, and perceived value as the pull factors.

Push effect

In the original meaning of PPM, migrants typically leave their place of origin, because they are not satisfied with the quality of life. Many previous studies emphasized the role of dissatisfaction on migration.¹¹ For instance, Zeng et al.¹³ found that users’ dissatisfaction with paper-based work patterns has a positive effect on their intention to switch to land information system (LIS). Fan et al.¹² also opined that system quality plays a key role in mobile payments and discovered that dissatisfaction with system quality positively affects the intention to switch from Internet payment to mobile payment. When users are dissatisfied with the original pattern or system, the switch intention is significant.

For the above reason, we take dissatisfaction with paper-based billing as the factor affecting switch intention. More specifically, this study regards when users are dissatisfied with the service of paper-based billing that it would increase their motivation to adopt electricity management systems from the original paper-based pattern. Accordingly, the following hypothesis is proposed.

H1: Dissatisfaction with paper-based billing positively influences users’ intention to switch to an electricity management system.

Pull effects

A high level of locatability means that users are able to get up-to-date information whenever they need as well as get timely information.¹⁷ Therefore, when users can get their electricity information anytime and anywhere, it makes things feel convenient, and they can perceive the value of the electricity app. Exploring consumer behavior in the switch between membership cards and mobile applications, Li¹⁶ discovered that locatability offers benefits compared to paper-based membership cards and has a significant impact on switching intention. Furthermore, Zeng et al.¹³ concluded when LIS provides land information and saves a lot of users’ time that it also makes users aware of the system's value and increases the possibility of switch behavior. Thus, this study regards that locatability influences perceived value positively and presents the next hypothesis.

H2: Locatability positively affects perceived value.

Information quality refers to the quality of information provided to the people by specific platforms or organizations.¹³ Delone and Mclean¹⁸ regarded that information quality is a key variable that impacts users’ intention to adopt a system. Zeng et al.¹³ also found that an information system has a positive effect on perceived usefulness and perceived ease of use. Furthermore, according to an online service, Chairina¹⁹ concluded that perceived usefulness (PU) and perceived ease of use (PEU) influence perceived value positively. Therefore, when users are aware that certain information is accurate, credible, as well as complete, it enhances PU and further improves the perceived value of an electricity management system. Hence, we view information quality as the pull factor to influence perceived value and propose the following hypothesis.

H3: Information quality positively affects perceived value.

Economic benefit could be defined as the monetary savings that consumers receive.¹⁶ Incorporated from a value maximization standpoint,²⁰ Jayashankar et al.²¹ showed that both monetary and non-monetary factors impact perceived value and further enhance IoT (Internet of Things) adoption in an industrial context. From the customer viewpoint, Fandos Roig et al.²² also proposed that perceived value is affected by benefits received, such as economic, social, and relational. An electricity app can utilize the relevant information to provide customized suggestions of time-of-use pricing for users as well as detect unusual electricity consumption. When users are aware about the potential advantages of using an electricity app and believe in its economic benefits, the perceived value of electricity management systems is improved. Therefore, this study takes economic benefits as the pull factor to influence perceived value and formulates the next hypothesis.

H4: Economic benefits positively affect perceived value.

The construct of perceived value has been viewed as an important factor of adoption, purchase, and continuance intention of usage in many areas.^16,23,24 When users are aware of the advantages of using an electricity app, it makes it easier for them to perceive the app's value and increases the possibility of switching from paper-based billing to the electricity app. For instance, if users perceive value coming from the app, such as getting electricity information in a timely manner, then it decreases their intention of using paper-based billing as well as raises their intention of adopting the electricity app. Wang et al.²⁴ proposed that when users perceive value from adopting an m-government system, such as personalization, security, and localizability, it enhances their continuance intention. In addition, El-Haddadeh et al.²³ revealed that perceived value positively affects the using intention of IoT-enabled services. Consequently, this study includes perceived value as a key factor to affect users’ intention to switch to an electricity app and offers the following hypothesis.

H5: Perceived value positively affects users’ intention to switch to an electricity management system.

Mooring effect

Agarwal and Prasad²⁵ regarded that individuals with high-level personal innovativeness are expected to adopt an innovation earlier. In addition, they defined the term of personal innovativeness of information technology (PIIT) as “the willingness of an individual to try out [a] new information system” (Agarwal and Prasad,²⁵ p. 206). Psychologists also believe that personal characteristic are much stable across usage settings.^26,27 Over the years, the idea of personal innovativeness has been adopted in exploring the attitudes or intention in many areas. Lu²⁷ proposed the results that PIIT will through PU and PEU affect mobile commerce continuance intention. Patil et al.²⁸ argued that personal innovativeness positively influences the attitude of mobile payment in India. Furthermore, one study provided support that personal innovativeness positively influences people's switch intention from Internet payment to mobile payment.¹² Thus, this study regards that compared to lower personal innovativeness, users with higher personal innovativeness have greater willingness and interest to adopt new technology, such as electricity app, and so the following hypothesis is formulated.

H6: Personal innovativeness positively influences users’ intention to switch to an electricity management system.

Many studies have viewed personal innovativeness as an important moderator. For example, Alkawsi et al.¹ showed that personal innovativeness positively moderates the effect of privacy concern on behavioral intention to use a smart meter. Jang and Lee²⁹ also regarded the relationships among reputation, trust, and entertainment and attitudes in the context of location-based service (LBS) that personal innovativeness is a strong moderating variable. People with high-level personal innovativeness means that they have a more optimistic attitude and are open-minded toward a new technology. Therefore, when users are dissatisfied with paper-based billing and discover the new technology of a power information platform, those with higher personal innovativeness will switch to adopt an electricity app more easily compared to those with lower personal innovativeness. Moreover, when users realize the benefits or values from an electricity app, those with high-level personal innovativeness will magnify the benefits. In other words, compared to low-level personal innovativeness, users with high-level personal innovativeness strengthen the positive effect of perceived value on switch intention, leading to the next hypothesis.

H7a: Personal innovativeness moderates the relationship between dissatisfaction and users’ intention to switch to an electricity management system.

H7b: Personal innovativeness moderates the relationship between perceived value and users’ intention to switch to an electricity management system.

Method

Data collection and participants

Because of the growing concerns with electricity supply, carbon reduction, and climate change issues, the Taiwan government and Taiwan Power Company (TPC) are putting much more resources into developing smart grids. With the help of them, residents can get up-to-date power information and understand the consumption of electricity through electricity management systems. In addition, TPC is employing modern technology to conserve electricity. For example, depending on different electricity habits, it provides suggestions to help people attain the goal of electricity conservation. Therefore, this study views Taiwan as a source and aims to figure out the crucial factors affecting switching intention from traditional paper-based billing to an electricity management system. The first section of the questionnaire is an introduction to explain the research objectivity and also introduces the electricity management system. The second section is for collecting the data for each variable in the research framework. The third section aggregates the participants’ demographic information.

The questionnaire was conducted as a pretest before formal data collection. This study invited 31 participants from power-related companies and academia. Based on the feedback from the pretest, this study made modifications in order to fit the context of this research. In addition, the alphas value of variables (dissatisfaction, personal innovativeness, locatability, information quality, economic benefits, perceived value, and switching intention) are 0.886, 0.742, 0.929, 0.916, 0.885, 0.926, and 0.965, respectively, or all higher than 0.7, representing that all variables have great reliability.

Excluding those with incomplete answers, in total 421 valid questionnaires were collected. Among the effective respondent rate of 84.2%, 40.9% are males and 59.1% are females. The majority of respondents are aged between 31 and 40 (48.2%), followed by 41–50 years old (24.9%) and 25–30 years old (16.9%). Nearly 74.3% of the respondents have a bachelor's degree. Approximately, 29.2% of the respondents’ monthly income is between NT$40,001 and NT$60,000.

Measurement

Our research includes five exogenous variables, two endogenous variables, and one control variable and looks to explore how exogenous variables impact endogenous variables. This study utilizes a seven-point Likert scale (1 = “strongly disagree”; 7 = “strongly agree”) to measure items, and the items for each construct are shown in Table 1.

Table 1.

Items for each construct.

Construct	Items
Dissatisfaction	I do not feel satisfied about my overall experience using traditional paper-based billing to deal with electricity issues
	I do not feel happy about my overall experience using traditional paper-based billing to deal with electricity issues
	Traditional paper-based billing does not meet various electricity requirements
Personal innovativeness	If I hear about new information technology, then I look for ways to experiment with it
	Among my peers, I am usually the first to try out new information technologies
	I like to experiment with new information technologies
Locatability	I am able to get up-to-date information whenever I need it with an electricity management systems
	I can get timely information with an electricity management systems
	I am able to access relevant information wherever I want with an electricity management systems
Information quality	The information provided by electricity management systems is accurate and credible
Information quality	The information provided by electricity management systems is complete and abundant
Economic benefits	I believe that the financial gain from using electricity management systems is worthwhile
	I have lower financial costs (monetary savings) by using an electricity management systems
	Overall, I save money by using an electricity management systems
Perceived value	I believe that using electricity management systems is valuable
	I believe that using electricity management systems is worthwhile
	I believe that using electricity management systems overall delivers good value
Switch intention	I will consider switching from traditional paper-based billing to electricity management systems
	The likelihood of my switching to electricity management systems is high
	I am determined to switch to electricity management systems

This study adopts five exogenous variables, including dissatisfaction, personal innovativeness, locatability, information quality, and economic benefits. We use a three-item scale to measure dissatisfaction,^30–32 a sample item is “I do not feel satisfied about my overall experience using traditional paper-based billing to deal with electricity issues.” This study takes three items developed by Agarwal and Jayesh²⁵ to measure personal innovativeness, a sample item is “If I hear about new information technology, then I look for ways to experiment with it.” Xu et al.¹⁷ developed three items scale to measure locatability, which we adopt, a sample item is “I am able to get up-to-date information whenever I need it with an electricity management systems.” Eldrandaly et al.³³ and Chi³⁴ measured information quality with a two-item scale, a sample item is “The information provided by electricity management systems is accurate and credible.” Moreover, this study measures economic benefits with the three-item scale proposed by Chang and Polonsky.³⁵ A sample item is “I believe that the financial gain from using electricity management systems is worthwhile.”

We also use two endogenous variables in our study, including perceived value and switch intention. Liu et al.³⁶ developed a three-item scale to measure perceived value, which we adopt, a sample item is “I believe that using electricity management systems is valuable.” This study also uses three items developed by Zhang et al.³⁷ to measure switch intention. A sample item is “I will consider switching from traditional paper-based billing to electricity management systems.”

Hou and Shiau¹⁵ proposed to consider the impact of gender when exploring the topic of switch intention. Therefore, we view gender as a control variable in our research model.

Data analysis

Validity and reliability

Before implementing confirmatory factor analysis and hypothesis analysis, we have to first confirm reliability and validity. This study adopts component reliability (CR) to examine reliability and displays the results in Table 2. The CR values of dissatisfaction, personal innovativeness, locatability, information quality, economic benefits, perceived value, and switching intention are 0.851, 0.850, 0.870, 0.883, 0.904, 0.891, and 0.891, respectively, or all higher than 0.7 and implying that each variable has great reliability (Hair et al., 2010). Furthermore, this study conducts convergence and discriminant tests to verify validity. For convergence validity, Hair et al.³⁸ regarded that average variance extracted (AVE) higher than 0.5 and CR higher than 0.7 represent great convergent validity. As shown in Table 1, each variable of AVE is bigger than 0.5, and the values of CR are over 0.7, denoting the variables own great convergent validity. For discriminant validity, each construct's squared root of AVE must be bigger than the correlation with other constructs in the research model.³⁹ The values in Table 3 reveal that all squared root values of AVE are higher than the correlation with other constructs, thus providing support that this study exhibits great discriminant validity.

Table 2.

Reliability and validity.

Variable	Construct	Loading	CR	AVE
DI	DI1	0.577	0.851	0.664
	DI2	0.930
	DI3	0.891
PI	PI1	0.771	0.850	0.655
	PI2	0.750
	PI3	0.900
LO	LO1	0.878	0.883	0.715
	LO2	0.826
	LO3	0.833
IQ	IQ1	0.903	0.870	0.770
IQ	IQ2	0.852	0.870	0.770
EB	EB1	0.869	0.904	0.758
	EB2	0.916
	EB3	0.825
PV	PA1	0.838	0.891	0.731
	PA2	0.867
	PA3	0.861
SI	SI1	0.856	0.891	0.732
	SI2	0.872
	SI3	0.839

Note: DI = dissatisfaction, PI = personal innovativeness, LO = locatability, IQ = information quality, EB = economic benefits, PA = perceived value, SI = switching intention.

Table 3.

Construct correlations.

Variable	Mean	SD	1	2	3	4	5	6	7
1. DI	4.796	1.148	0.814
2. PI	5.504	0.953	0.234	0.809
3. LO	5.245	1.014	0.166	0.406	0.845
4. IQ	5.604	0.971	0.240	0.391	0.682	0.877
5. EB	5.356	1.050	0.280	0.428	0.452	0.513	0.870
6. PV	5.711	0.834	0.223	0.445	0.585	0.738	0.576	0.854
7. SI	5.728	0.911	0.274	0.525	0.542	0.664	0.585	0.781	0.859

Note: The figures in the diagonal text represent the root of AVE values for each construct. DI = dissatisfaction, PI = personal innovativeness, LO = locatability, IQ = information quality, EB = economic benefits, PV = perceived value, SI = switching intention.

Confirmatory factor analysis

We employ five measurement models to verify that the research model (seven-factor model) is the best: (1) one-factor model (combining all items into one construct); (2) four-factor model (combining locatability, information quality, economic benefits, and perceived value into one construct); (3) five-factor model (combining locatability, information quality, and economic benefits into one construct); (4) six-factor model (combining locatability and information quality into one construct); (5) seven-factor model (the research model). Through the comparative fit index (CFI) index and chi-square different test, the results in Table 4 reveal that our research model (seven-factor model) is better than the four alternative models.

Table 4.

The results of confirmatory factor analysis.

Model	χ2	df	CFI
1. One-factor model	2352.128	170	0.647
2. Four-factor model	1295.812	164	0.817
3. Five-factor model	1127.911	160	0.844
4. Six-factor model	561.400	155	0.934
5. Seven-factor model	418.210	149	0.957
Chi-square model comparison tests
Δ Model	Δχ2	Δdf	Chi-square critical value
Model 1 vs. Model 5 Model 2 vs. Model 5 Model 3 vs. Model 5 Model 4 vs. Model 5	1933.918 877.602 709.701 143.19	21 15 11 6	32.077 24.384 19.045 11.911

Note: CFI = comparative fit index.

This study also examines the goodness of fit to evaluate the research model. The fit of our seven-factor model to the data is high: the chi-square value is 2.807, or slightly less than 3.⁴⁰ The goodness-of-fit index (GFI), CFI, incremental fit index (IFI), and Tucker–Lewis index (TLI) values are 0.907, 0.957, 0.957, and 0.945, respectively, representing a good model fit.^41,42 Moreover, the root-mean-square error of approximation (RMSEA) is 0.066, or less than 0.08, also indicating a good model fit.

Results of hypotheses

Figure 2 displays the results among dissatisfaction, personal innovativeness, locatability, information quality, economic benefits, perceived value, and switch intention. These results show that dissatisfaction has no positive influence on switch intention (β = 0.056, t = 1.513), thus Hypothesis 1 is not supported. Hypotheses 2, 3, and 4 postulate positive effects of locatability, information quality, and economic benefits on perceived value, respectively. Our results show that locatability (β = 0.603, t = 9) and economic benefits (β = 0.218, t = 6.055) have a positive effect on perceived value, however, information quality has no significant effect on perceived value (β = −0.009, t = 0.163). Therefore, the results do not support Hypothesis 3, but do support Hypotheses 2 and 4. Hypothesis 5 aims to verify the positive impact of perceived value on switch intention, and the result reveals that perceived value does influence switch intention positively (β = 0.8825, t = 16.346), thus supporting Hypothesis 5. Hypothesis 6 postulates that personal innovativeness has a positive effect on switch intention, and the results display that personal innovativeness does influence switch intention significantly (β = 0.244, t = 5.674), thus supporting Hypothesis 6.

In Hypotheses 7a and 7b, we postulate that personal innovativeness moderates the relationship between dissatisfaction and switch intention as well as perceived value and switching intention. The result in Hypothesis 1 shows that dissatisfaction has no significant effect on switching intention. Furthermore, no matter for high-level personal innovativeness (β = 0.041, t = 0.872) or low-level personal innovativeness (β = 0.063, t = 0.926), the results show that dissatisfaction does not significantly impact switch intention, and thus Hypothesis 7a is not supported.

This study follows the processes used by Singh⁴³ to adopt the chi-square difference test in order to compare the constrained and unconstrained models. The results in Table 5 display that personal innovativeness moderates the relationship between perceived value and switch intention, thus supporting Hypothesis 7b. More specifically, we discover that high-level personal innovativeness (β = 0.799, t = 13.316) has a much greater influence in the relationship between perceived value and switching intention than low-level personal innovativeness (β = 0.745, t = 6.834).

Table 5.

The results of the moderating effect.

Chi-square test for moderating effect
	χ2	df	Δχ2 (Δdf)	Chi-square critical value
Model (PV → SI)
Unconstrained model	731.759	346	7.239(2)	χ² (2,0.05) = 5.991
Constrained model	738.998	348	7.239(2)	χ² (2,0.05) = 5.991
		Estimate	SE	t-value
PV → SI	Group 1	0.745	0.107	6.834
PV → SI	Group 2	0.799	0.068	13.316

Notes: DI = dissatisfaction, PV = perceived value, SI = switch intention. Group 1 = low level of personal innovativeness, and Group 2 = high level of personal innovativeness.

Discussion

With growing concerns about power and environment issues, this research provides informative results to fill the gap in the literature concerning smart grid and government policy. Based on the PPM model, this study explores the effect of push, pull, and mooring in order to figure out the important antecedents for enhancing users’ intention toward adopting electricity management systems. The results display that dissatisfaction has no positive influence on switch intention. Through perceived value, the variables of locatability and economic benefits influence switch intention. In addition, as a mooring variable, personal innovativeness not only impacts switch intention positively, but also moderates the relationship between perceived value and switch intention. However, information quality has no significant effect on perceived value.

Theoretical and practical implications

The results show that dissatisfaction has no significant effect on switch intention. Even though there is a lot of inconvenience from paper-based billing, such as easy to lose a paper bill and not getting electricity information in a timely manner, people are still accustomed to using paper-based billing and generally have no motivation to use electric management systems. The research of Sun et al.³² also showed that dissatisfaction has no significant impact on mobile instant messaging applications. Therefore, to encourage consumers to adopt electricity management systems, it is not just helpful to emphasize the inconvenience of paper-based billing. Rather, for the sake of attracting more people to use electricity management systems, governments can place more attention on the pull and mooring effects.

For the pull effect, the results show through the perceived value that locatability and economic benefits affect users’ switching intention. Compared to using traditional paper billing, when users adopt electricity management systems and understand that they are able to get up-to-date electricity information whenever and whatever they want, they will experience the benefits from electricity management systems. Therefore, such an experience will increase the perceived value of an electricity management system and push their motivation to adopt it, and so people will have a higher switch intention from paper-based billing to electricity management systems. In addition, electricity management systems not only provide electricity information details that allow users to trace their electricity consumption, but also offer suggestions based on different consumption habits. For example, based on users’ habits power companies can provide the time-of-use (TOU) rates to decrease the consumption of electricity. Consequently, this will make users feel that this is beneficial for their own personal reasons as well as further influence their switch intention. If governments want to enhance people's motivation to adopt electricity management systems, then they can provide assurances that consumers are able to get up-to-date electricity information anytime and anywhere. Moreover, they also could emphasize that using electricity management systems has advantages at saving and controlling consumers’ electricity expenses.

From the viewpoint of the mooring effect, this study not only shows that personal innovativeness has a positive effect on switch intention, but also moderates the relationship between perceived value and switch intention. People with high-level personal innovativeness enjoy trying out or experimenting with new technology. Therefore, compared to consumers with lower-level personal innovativeness, consumers with higher personal innovativeness have more motivation and possibility to adopt electricity management systems. In addition, if people are aware about the value of such system, then compared to lower-level personal innovativeness, people with high-level personal innovativeness will see a strengthened positive effect of perceived value on switch intention. Thus, to encourage people to use electricity management systems, governments can publicize electricity management systems through different channels, such as social media, news, magazines, or podcasts. This will benefit in attracting those people with higher personal innovativeness to try out the service.

Limitations and future direction

Although this study provides some contributions in the area of electricity, smart grid, and policy, it still has some limitations. First, some important potential factors are not considered in this research model, such as environment or substitutability, and so other independent variables should be included in future research. Second, this research adopts the PPM theory as our foundation to develop the framework. Further research could include other theories to explore the issue of technology adoption, such as theory of reasoned action or technology acceptance model (TAM). Third, the respondents in this research are restricted to Taiwan. The effect of variables on people’s switch intention might vary in different cultures. Therefore, a cross-cultural design could be developed to provide broader views with regard to electric app switch intention. Finally, this study displays that dissatisfaction has no influence on switching intention, further research might involve other important push variables, such as fatigue and security risk.⁴⁴

Figure 2.

Results of the research model.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science and Technology (grant number MOST 110-2410-H-011-006).

ORCID iD

Chien-Wei Ho

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Motivating people to adopt electricity management system based on push–pull–mooring model

Abstract

Keywords

Introduction

Literature review

The push–pull–mooring theory

Push effect

Pull effects

Mooring effect

Method

Data collection and participants

Measurement

Data analysis

Validity and reliability

Confirmatory factor analysis

Results of hypotheses

Discussion

Theoretical and practical implications

Limitations and future direction

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References