Adoption of Sustainable Technologies: A Mixed-Methods Study of German Households

Wunderlich, Philipp; Veit, Daniel; Sarker, Saonee

doi:10.25300/misq/2019/12112

Cited by 99 publications

(59 citation statements)

References 9 publications

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“…The sampling was based on a non-probabilistic procedure. The questionnaire was published via the internet, similar to other surveys about smart meters (e.g., Vassileva and Campillo, 2016;Chen et al, 2017;Wunderlich et al, 2019). To ensure the sample was reflective of the population socio-demographics, respondents were invited to participate in the study using a variety of mediums.…”

Section: Data Collectionmentioning

confidence: 99%

Getting smarter about household energy: the who and what of demand for smart meters

Fettermann

Borriello

Pellegrini

et al. 2020

Building Research & Information

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The development of policies promoting smart meter adoption is essential to guide the transition towards sustainable use of resources such as water, electricity and gas, as well as inform smart-city initiatives. This article explores household preferences in terms of different smart meters and identifies the amounts that households are willing to pay for different smart meter configurations to monitor electricity, water and gas based on the features of their home including dwelling type, size and property value. To this aim, we employ a mixed multinomial logit model that accounts for the heterogeneity in customers' preferences for different smart meters. As a proof of concept, the proposed model is applied to a survey incorporating a discrete choice experiment carried out with 232 respondents in the Florianopolis metropolitan region, located in the south of Brazil.Our approach offers a number of advantages to facilitate the broader implementation of smart grid systems that would otherwise be overlooked using traditional approaches that rely on aggregated estimates for demand and willingness to pay for proposed schemes.

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Section: Data Collectionmentioning

confidence: 99%

Getting smarter about household energy: the who and what of demand for smart meters

Fettermann

Borriello

Pellegrini

et al. 2020

Building Research & Information

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“…In Younger age groups were more like to adopt energy monitoring (Chen and Sintov, 2016) and smart meter technology (Wunderlich et al, 2019), whilst older people in the UK less likely to adopt smart electricity meter (Buchanan et al, 2016). Other studies show no effect (Chen et al, 2017;Nasir et al, 2020;Fettermann et al, 2021) Gender Binary (Male or female) Not typically a significant predictor (Chen and Sintov, 2016;Chen et al, 2017;Nasir et al, 2020;Fettermann et al, 2021).…”

Section: Dependent Variables Descriptionmentioning

confidence: 96%

“…Other studies show no effect (Chen et al, 2017;Nasir et al, 2020;Fettermann et al, 2021) Gender Binary (Male or female) Not typically a significant predictor (Chen and Sintov, 2016;Chen et al, 2017;Nasir et al, 2020;Fettermann et al, 2021). Number of females predicts shower water usage (Makki et al, 2013) Highest level of education Three categories (secondary or below, A-levels, university)-dummy variables Not a significant predictor (Chen and Sintov, 2016;Bugden and Stedman, 2019;Wunderlich et al, 2019;Nasir et al, 2020). High educated respondents more likely to accept digital water meter (Koop et al, 2021) Household characteristics children (under 18) living in the household Binary (Yes or no) Household size not significant (Chen and Sintov, 2016;Wunderlich et al, 2019;Fettermann et al, 2021).…”

Section: Dependent Variables Descriptionmentioning

confidence: 96%

Public Perceptions of Household IoT Smart Water “Event” Meters in the UK—Implications for Urban Water Governance

Goulas

Goodwin

Shannon

et al. 2022

Front. Sustain. Cities

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Cities around the world are facing water availability challenges, intensified by increasing populations and climate change. Technology, such as household smart meters measuring domestic water consumption, can play a role in demand management, yet a deeper understanding of public expectations and the practicalities of city-wide implementation is required. This article explores public perceptions of smart water meters that use Internet of Things (IoT) technology and machine learning to profile household water use “events” and anomalies. By leveraging insights from an online survey implemented in the UK (n = 558), this article explores factors influencing the likelihood of citizens choosing to have this type of meter installed along with potential societal barriers and opportunities. Nearly half of the participants said they would choose to have such a meter installed and logistic regression showed predictive variables were younger ages, being male, those with existing water meters and those with other smart devices. The likelihood of choosing this type of water meter was also associated with preferences to have control over data privacy, whether the meter would reduce water bills and whether it was provided free of charge. We locate these results within other contemporary experiences of smart meters and water grids in urban contexts to discuss practical challenges of using real-time environmental data for urban water governance. Policymakers and water resources planners should continue to monitor public perceptions, implement urban experiments and cost-benefit analyses to better interpret the wider benefits of such technology for behavioral and educational interventions within a more digitized and increasingly data-centric water grid.

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“…The authors followed the instructions for interpretive research from Klein and Myers (1999) to complete the data analysis. The authors used an inductive paradigm to make sense of the qualitative data (Wunderlich et al, 2019). Following Klein and Myers (1999), the authors adopted dynamic adjustment on the interview questions based on their interaction with the participants and iterated between the data and literature in the coding process, yielding three rounds of analysis.…”

Section: Study 1 Qualitative Studymentioning

confidence: 99%

Exploring the Impact of Country-of-Origin Image and Purchase Intention in Cross-Border E-Commerce

Bao

Cheng

Zarifis

2021

Journal of Global Information Management

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Product country-of-origin (COO) is now playing a central role in consumers’ purchase behavior. Previous studies have investigated several factors that impact COO. However, little attention has been paid to the impact of COO on consumers’ product evaluation on Chinese products, especially in the cross-border e-commerce context. Using a multi-methods design, this study first unearthed the antecedents of COO image towards Chinese products from the qualitative data in Study 1 by drawing on the legitimacy theory and then develops a contextual model of consumers’ product evaluation and purchase intention, integrating the role of a product with a different level of involvement. Using quantitative survey data from 252 foreign consumers, the study tests the research model in Study 2. The findings provide empirical evidence to support the model and highlight the importance of COO cues on foreign consumers’ purchase intention towards Chinese products. The results also enhance our understanding of consumers’ purchase decision in cross-border e-commerce.

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Adoption of Sustainable Technologies: A Mixed-Methods Study of German Households

Cited by 99 publications

References 9 publications

Getting smarter about household energy: the who and what of demand for smart meters

Getting smarter about household energy: the who and what of demand for smart meters

Public Perceptions of Household IoT Smart Water “Event” Meters in the UK—Implications for Urban Water Governance

Exploring the Impact of Country-of-Origin Image and Purchase Intention in Cross-Border E-Commerce

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