2022
DOI: 10.1002/cb.2051
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Consumer matching costs to context: Status quo bias, temporal framing, and household energy decisions

Abstract: One challenge of promoting energy-efficient behavior change is status quo bias: consumers are reluctant to change away from their current level of energy usage, even if their energy usage is higher than they would actively choose. Using experimental data, this article examines what temporal frame minimizes status quo bias and encourages energy-efficient behavioral intentions. The authors find that individuals make the most energy efficient decisions when presented with a monthly framing because they are more e… Show more

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Cited by 4 publications
(6 citation statements)
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References 69 publications
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“…The SQB theory suggests that users are showing unfavorable responses toward new systems due to the existing system's status quo (Samuelson & Zeckhauser, 1988). Cognitive misperceptions (inertia, perceived values), rational decision making (switching cost and perceived threat), and psychological commitment sunk cost, regret avoidance) These are crucial inhibitors to adopting new technology (Gill et al, 2022; Joshi et al, 2021). SQB theory has been extensively used in understanding emerging technology adoption such as voice assistance (Balakrishnan et al, 2021), cloud system (Fan et al, 2015), mobile ticketing (Zhao et al, 2016), health cloud (Hsieh, 2015) and knowledge management systems (Li et al, 2016).…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…The SQB theory suggests that users are showing unfavorable responses toward new systems due to the existing system's status quo (Samuelson & Zeckhauser, 1988). Cognitive misperceptions (inertia, perceived values), rational decision making (switching cost and perceived threat), and psychological commitment sunk cost, regret avoidance) These are crucial inhibitors to adopting new technology (Gill et al, 2022; Joshi et al, 2021). SQB theory has been extensively used in understanding emerging technology adoption such as voice assistance (Balakrishnan et al, 2021), cloud system (Fan et al, 2015), mobile ticketing (Zhao et al, 2016), health cloud (Hsieh, 2015) and knowledge management systems (Li et al, 2016).…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…The results showed that when presented with a monthly framework, individuals made the most energy efficient decisions because people were more likely to consider monthly costs than annual or daily costs. They also argue that people are usually unwilling to change the current level of energy use, even if they are using more energy than they actively choose [12].…”
Section: Energy Consumptionmentioning
confidence: 99%
“…Gill et al used a time frame model and concluded that when developing a monthly framework, individuals are able to make decisions that minimize energy consumption [12]. But direct evidence for status quo bias and energy consumption is also lacking.…”
Section: Energy Consumptionmentioning
confidence: 99%
“…The time-inconsistent preference bias refers to a situation when people perceived things as less valuable or less significant if further away in time, even if such things afford long-term benefits (Thaler R. , 1981); (Gill, Atlas, & Hardisty, 2016); (Hardisty, Shim, Sun, & Griffin, 2017). For instance, people often 'discount the future' by preferring smaller immediate rewards (e.g., $5 now) over larger future rewards (e.g., $10 next year), while they may avoid actions which are costly in the short-term (e.g., outlaying money and time to purchase new energy-efficient appliances, or making an effort to switch energy retailers), despite offering larger long-term benefits (e.g.…”
Section: Time Inconsistent Preferencementioning
confidence: 99%
“…Previous studies suggested that effective interventions should connect consumers with their 'future selves' which lead to larger contributions to savings and future rewards (Hershfiled et al, 2011); (Gill, Atlas, & Hardisty, 2016). This implies that future rewards from consumers' purchase decisions should be available to them.…”
Section: Knowledge Gap In Led Product Adoptionmentioning
confidence: 99%