# Understanding and Influencing End-User Behavior in Software Energy Consumption

Thomas Zaragoza, Thibault Soulancé, Adel Noureddine, Ernesto Exposito

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Thomas Zaragoza, Thibault Soulancé, Adel Noureddine, Ernesto Exposito. Understanding and Influencing End-User Behavior in Software Energy Consumption. EASE'25: 29th International Conference on Evaluation and Assessment in Software Engineering (, Jun 2025, Istanbul, Turkey. hal-05008468

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# Understanding and Influencing End-User Behavior in Software Energy Consumption

Thomas Zaragoza

thomas.zaragoza@univ-pau.fr

Université de Pau et des Pays de l'Adour, E2S UPPA,
LIUPPA

Pau, France

Adel Noureddine

adel.noureddine@univ-pau.fr

Université de Pau et des Pays de l'Adour, E2S UPPA,
LIUPPA

Pau, France

Thibaut Soulance

thibaut.soulance@gmail.com

Université de Pau et des Pays de l'Adour, E2S UPPA,
LIUPPA

Pau, France

Ernesto Exposito

ernesto.exposito@univ-pau.fr

Université de Pau et des Pays de l'Adour, E2S UPPA,
LIUPPA

Pau, France

## ABSTRACT

With growing concerns about computing's environmental impact, sustainable IT practices must engage all stakeholders—including end-users. This study investigates how user behavior influences software energy consumption through a mixed-methods approach: a campus survey (N=280) revealed that while 91.5% understand device energy use, only 55.3% recognize software's role, and just 46.8% modify habits despite 79.3% acknowledging individual responsibility. Follow-up interviews highlighted demographic divides, with older (25-64) and advanced-degree users showing greater awareness. A week-long field experiment at an IT company demonstrates that real-time feedback—especially disaggregated data showing top energy-consuming apps—shifted perceptions, reducing hardware's perceived influence by 29%. Though users adopted energy-saving actions (e.g., closing idle apps, switching to lightweight software), workplace priorities often limited sustained changes. Our findings prove user-centric interventions can raise awareness, but highlight the need for context-sensitive designs that balance energy efficiency with workflow demands. This work establishes actionable pathways to reduce ICT's carbon footprint through behavior-aware software ecosystems.

## CCS CONCEPTS

* Hardware → Impact on the environment; * Applied computing → Sociology; * General and reference → Experimentation; Empirical studies; General conference proceedings; * Social and professional topics → User characteristics; * Human-centered computing → Visualization.

## KEYWORDS

Energy consumption, Software Engineering, Survey, Feedback, Field Studies, Behavioral Studies

### ACM Reference Format:

Thomas Zaragoza, Thibaut Soulance, Adel Noureddine, and Ernesto Exposito. 2025. Understanding and Influencing End-User Behavior in Software Energy Consumption. In *Proceedings of The 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025)*. ACM, New York, NY, USA, 11 pages. https://doi.org/XXXXXXX.XXXXXXX

## 1 INTRODUCTION

The proliferation of end-user devices such as personal computers, smartphones, and tablets has transformed modern life, making information and communication technology (ICT) an indispensable part of our daily routines. As these devices become more integrated into our lives, their cumulative energy demand has grown. ICT's greenhouse gas emissions (GHGE) are estimated to be around 2.1% to 3.9% in 2020 [12], which can be further decomposed into around 30% originating from embodied emissions (i.e., production, transportation, use, and disposal) and around 70% coming from the use phase emissions (i.e., emissions associated with the operation and utilization of a product or service throughout its intended lifespan, in particular energy-related emissions from electrical consumption of computing devices). ICT's GHGE is expected to rise to 14% in 2040 [6]. ICT's electric consumption is estimated at around 7% of global electricity in 2020, and is expected to continue rising [27]. Software plays a crucial role in how efficiently these devices operate. The design and usage of which impact how energy consuming they can be. For instance, software that runs complex algorithms in the background, frequently update or synchronize data over a network, and games that require high processing power can all contribute to higher energy use. Therefore, the way end-users interact with and manage software on their devices directly impacts the energy footprint of ICT. Despite the increasing energy demands of ICT, the sector is often seen as a tool for promoting sustainability in other fields. IT solutions have been leveraged to enhance efficiency, reduce waste, and lower emissions in industries ranging from transportation to manufacturing. This perception has led to a tendency to overlook the growing energy consumption within the ICT sector itself. Even when the environmental impact of ICT is acknowledged,

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<footnote>EASE 2025, 17–20 June, 2025, Istanbul, Türkiye</footnote>
the focus tends to be on industry-side solutions such as software optimization or hardware improvements, rather than on end-users. This stems from a reluctance to burden end-users with the responsibility of energy conservation. It is well documented that humans have a fundamental tendency to overuse, often exacerbated by the rebound effect — where the introduction of a more efficient technology leads to increased overall consumption because the cost savings result in more usage. This is illustrated in the Jevons Paradox, an extreme example originating from William Stanley Jevons, a 19th-century economist, who observed that improvements in coal-powered technologies did not decrease coal consumption as anticipated, but instead drastically increased its consumption due to expanded usage [14]. Addressing this issue requires a comprehensive approach that not only improves the efficiency of software and hardware but also enhances end-user awareness and modifies usage behavior. By understanding how end-users perceive energy consumption in software and evaluating the effectiveness of feedback mechanisms to promote energy-efficient practices, we can develop strategies that harness both technological advancements and user engagement to mitigate the environmental impact of ICT. In this study, we explore two key research questions: **RQ 1. What is the end-user's perspective on energy consumption in software?** This research question investigates how end-users perceive and respond to the energy consumption of software. To analyze user perspectives on software energy consumption, we have created three theoretically-grounded constructs, integrating concepts from previous works on the subject [8] [1]:

C1: Perception: Users' awareness and attention to the concept of energy consumption in ICT and software.

C2: Comprehension: Users' understanding of the causes, variability, and technical aspects of energy consumption in software.

C3: Actions: The concrete behavioral responses or changes made by users to reduce energy consumption.

These constructs examine users' awareness, attitudes, and concerns about software's energy efficiency, including its importance in their software choices, their knowledge of its impact, and which aspects of energy consumption matter most to them.

**RQ 2. Can feedback raise awareness and eventually reduce energy consumption?** This research question explores whether feedback on energy consumption can enhance user awareness and reduce energy use. It investigates the effectiveness of feedback mechanisms, like real-time visualizations, in promoting energy-efficient behaviors. The goal is to assess whether feedback can sustain changes in energy usage and identify the most effective feedback types.

To answer these questions, we conducted a campus-wide survey with over 286 responses from students, teachers, researchers and staff members, followed by interviews and a week-long field experiment in a software company where software energy consumption was monitored, and users received real-time feedback. This dual approach aims to provide a comprehensive understanding of end-users' relationship with software and the potential for user-centered interventions in reducing the energy footprint of software. The remainder of this paper is organized as follows: in Section 2, we explore related research on software energy and feedback approaches. We present our experimental protocol and methodology

for the survey and the field study, in Section 3. We present the results of our experiments along with our analysis and answers to each of our research questions in Sections 4 and 5. Finally, we conclude and present our future directions in Section 8.

## 2 RELATED WORK

In recent years, research has highlighted the effectiveness of integrating feedback, gamification, and social interactions to influence user behavior. Feedback mechanisms like real-time notifications and performance analytics provide immediate insights, enhancing self-awareness and enabling timely adjustments. These mechanisms often use metaphors, such as trees [31], and can lead to significant savings [29]. By incorporating gamification elements like points, badges, and challenges, it taps into the human drive for achievement and competition, making tasks more engaging and encouraging sustained participation [21]. For instance, the game *Energy Chickens* reduced office energy consumption by linking workers' energy use to the evolution of virtual pets, rewarding energy-saving behaviors with in-game currency [19]. Social elements in feedback leverage social influence and peer pressure to encourage desired behaviors. *EnergyWiz*, for example, allows users to compare their energy consumption with peers, promoting energy-saving practices through social platforms [22]. This approach is also effective in education, health, and sustainability, where engagement and long-term commitment are crucial [15, 30]. Educational platforms use gamified quizzes and peer discussions to enhance learning outcomes [4], while fitness applications employ social challenges and progress tracking to encourage exercise [17]. In sustainability initiatives, gamified feedback on energy consumption, combined with social norms and community goals, has significantly reduced household energy use. For instance, the *Dubuque Electricity Portal* provided detailed feedback on electricity consumption, resulting in a 3.7% reduction, and an increased understanding of energy use [9].

The *Green Lift* intervention provided real-time CO₂ emissions data to encourage stairs use during high-emission periods, which prompted increased stairs use despite no significant energy reduction [26]. Further pushing the boundaries of feedback, Augmented Reality (AR) has been used to enhance awareness of energy consumption. An AR-based mobile application designed to educate users about the energy consumption of electronic devices showed a positive correlation between AR and improved understanding of sustainable device usage [5].

Conversely, while there has been extensive exploration of energy consumption in smart homes, the focus on ICT has traditionally been more technical. Literature has predominantly addressed strategies for optimizing software and hardware to reduce energy use, often neglecting the role of end-users in shaping consumption patterns. Expert-driven solutions have concentrated on software efficiency and hardware advancements, such as in [3] where a conceptual green framework was proposed for energy-efficient software development. Also, in [11], the authors proposed a comprehensive system energy model based on non-intrusive measurements for integrating into fast-cycle simulation tools, providing energy consumption feedback for embedded systems software programming. In mobile development, probabilistic techniques have been proposed for fine-grained energy estimation of Android application
methods [10]. Additionally, the authors in [13] conducted a case study on raising awareness of energy consumption among stakeholders in software development using an energy dashboard, which, despite mixed reactions, successfully prompted discussions about software energy use and highlighted the importance of awareness for meeting sustainability goals. Similarly, in [16], the authors conducted an empirical study on how software engineers consider energy usage throughout the software development lifecycle, based on a survey of 464 practitioners and 18 in-depth interviews. Their findings, backed by other studies [20, 25], offer insights, targeted towards developers and practitioners, into current practices, con-textualize existing research, and suggest directions for improving software energy efficiency.

Studying the relation between software usage by end-users and energy consumption is a recent research direction, with one notable recent study. In [18], the author conducted a field study on 100 computer science students over a 2-hour lab work session. A visual feedback tool presented their energy consumption in a combined feedback (a graph, and 3 metrics). The authors found that feedback helped raise awareness for end-users but they lacked the knowledge to make lasting behavioral changes.

In conclusion, the integration of feedback, gamification, and social elements demonstrate considerable success in influencing user behavior across various domains, including education, health, and environmental sustainability. These strategies effectively engage users by enhancing self-awareness, leveraging competitive instincts, and fostering social accountability. While significant progress has been made in understanding and optimizing energy consumption through technical solutions, the role of end-users in shaping these consumption patterns remains under-explored. Differing from related works, our study shifts focus from reducing ICT hardware energy consumption to addressing software energy use by providing user feedback to raise awareness and drive behavioral change. It advances the field with a survey of 286 participants (students, staff, and professionals across social sciences, natural sciences, and computing) exploring perceptions, understanding, and actions related to software energy consumption. A subsequent one-week field study connects these insights to practical steps for reducing software energy use.

# 3 METHODOLOGY

Our study consists of two parts: a survey and in-person interviews with around 286 respondents from students, staff and other members of a French university, and a field experiment in a French IT and software company, Constellation, where we monitor energy consumption and provide users with visual feedback on their software energy.

## 3.1 Campus-Wide Survey and Interviews

Our campus-wide survey study comprises an online questionnaire and a series of in-person interviews, which we describe in the following sections.

3.1.1 *Research Design.* We obtained approval from our university's ethics committee and legal department to ensure compliance with ethical standards and the EU's GDPR regulation. The survey, created using Sphinx software, was hosted on our university's intranet for

five weeks, with an email campaign inviting all students, staff, technicians, teachers, researchers, and professors to participate anonymously and voluntarily.

The survey questions serve two purposes: 1) to profile users and 2) to assess perspectives on software energy consumption. The profiling section is divided into three parts: socio-demographics, environmental attitudes, and technology familiarity. The socio-demographics section includes variables such as gender, age, socio-professional category, highest diploma, field of study, and role (e.g., staff or student), based on INSEE guidelines (National Institute for Statistics and Economic Studies, a French national statistical institution). The environmental attitudes section presents 22 statements based on the New Environmental Paradigm (NEP) [2], answered on a Likert scale without a neutral option, alternating between 12 negative and 10 positive statements to ensure attentiveness. The technology familiarity section contains seven statements, six answered on a Likert scale with alternating sentiments, and one estimating screen time in 2-hour intervals. To assess perspectives on energy consumption, the second section includes 16 statements categorized by constructs (see Tab. 1 and Section 1), plus two ranking tasks: the impact of hardware, software choice, usage, and peripherals, and the impact of software types on energy consumption. To supplement the survey findings, five semi-structured interviews [24] (50–80 minutes each) were conducted, recorded, and anonymized with participants’ consent. These interviews follow the same structure as the surveys, exploring participants’ relationships with the environment and technology before discussing their views on energy consumption. Candidates for the interview were recruited by canvassing the campus on foot with profiles reflecting that of the surveys.

3.1.2 *Participants.* To address our research questions, we focus on the academic sector. By narrowing our audience, we access a sufficiently large and diverse panel of users to cover our research topic. Comprising about 40% students and 60% staff, our sample include nearly 300 individuals who regularly use computing tools. This heterogeneous population includes 40% men, 59% women, and 1% others; 30.7% were aged 18-24, 13.9% aged 25-34, 16.1% aged 35-44, 24.6% aged 45-54, 14.3% aged 55-64, and 0.4% aged 65 or older. The study fields are represented as follows: 14% from education, 37% from natural and technological sciences, 8.2% from economics and management, 14.3% from humanities and social sciences, 8.2% from law and political science, 8.6% from arts, literature, and languages, 1.8%, from health and 1.4% from sports. Socio-professional categories, as defined by the INSEE to characterize a population, includes 52.1% executives and higher intellectual professions, 26.4% employees, 14.6% intermediate professions, 3.9% tradespeople, 2.1% workers, and 0.7% farmers. Educational levels includes 22.1% with PhDs, 33.6% with master's degrees, 12.9% with bachelor's degrees, 15% with two-year college degrees, 13.6% with high school diplomas, and 2.8% with lower education levels. Education classification is based on the ISCED statistical framework (International Standard Classification of Education). Groups that are too small are excluded from the analysis.

3.1.3 *Data Analysis.* To profile candidates, socio-demographic variables are treated as categorical, while environmental and technological scores are computed from the 22 and 4 Likert-scale responses
<table><caption>Table 1: Statements per construct in campus-wide survey</caption><thead><tr><th>Construct</th><th>Number</th><th>Statement</th></tr></thead><tbody><tr><td>C1</td><td>1</td><td>When using computer devices, it is important and necessary to be mindful of their energy consumption.</td></tr><tr><td>C1</td><td>2</td><td>I consider myself mindful of my energy consumption when using computer devices.</td></tr><tr><td>C2</td><td>3</td><td>I know what is referred to when we discuss the energy consumption of computer devices.</td></tr><tr><td>C2</td><td>4</td><td>I can interpret signs of significant energy consumption during my use of computer devices.</td></tr><tr><td>C2</td><td>5</td><td>The energy consumption of my computer devices varies depending on how I use them.</td></tr><tr><td>C2</td><td>6</td><td>The energy consumption of my computer devices varies depending on whether they are plugged into a power source or not.</td></tr><tr><td>C2</td><td>7</td><td>The addition of equipment and multimedia (headphones, audio headsets, Bluetooth connections, etc.) impacts the energy consumption of my computer devices.</td></tr><tr><td>C2</td><td>8</td><td>I know what my energy consumption represents (what it equals) when using computer devices.</td></tr><tr><td>C2</td><td>9</td><td>Among the following response options, rank from 1 to 4 the factors that, in your opinion, have the greatest impact on the energy consumption of IT devices. (Hardware components, software choice, software usage/tasks and peripheral equipment)</td></tr><tr><td>C2</td><td>10</td><td>I know what is referred to when we talk about software.</td></tr><tr><td>C2</td><td>11</td><td>I often use software when I use a computer device.</td></tr><tr><td>C2</td><td>12</td><td>I know how to define what computer software is.</td></tr><tr><td>C2</td><td>13</td><td>I can give examples of computer software.</td></tr><tr><td>C2</td><td>14</td><td>I know what is referred to when we talk about the energy consumption of computer software.</td></tr><tr><td>C2</td><td>15</td><td>The energy consumption of software is independent of the energy consumption of computer devices.</td></tr><tr><td>C2</td><td>16</td><td>Among the proposed options, rank from 1 to 8 the types of software according to their energy consumption. (Browser, communication, development, entertainment, editing/media, productivity tools, utilities, etc.)</td></tr><tr><td>C3</td><td>17</td><td>I regularly take actions to reduce my energy consumption when using computer devices.</td></tr><tr><td>C3</td><td>18</td><td>User efforts are not useful because it is a matter of engineering and science to reduce the energy consumption of our computer devices.</td></tr></tbody></table>

(plus screen-time estimate) to environmental and technological statements. For positive sentiment questions, answers are scored as -2, -1, 1, and 2 points for 'Strongly disagree,' 'Disagree,' 'Agree,' and 'Strongly agree,' respectively. For negative sentiment questions, the scoring is reversed. The screen-time estimate is scored based on daily usage intervals (0–2 hours = 1 point, 10+ hours = 5 points). After computation, environmental scores range from -44 to 44, and technological scores from -11 to 17. Candidates are divided into quartiles based on these scores to identify subgroups aligned with pro-environmental and/or pro-technological perspectives. Once profiling is complete, socio-demographic, environmental, and technological variables are cross-referenced with construct statement responses using a Python script to calculate Likert-scale answer percentages. These statistics are reviewed to identify significant differences across populations. The interviews were recorded, transcribed using professional software, and the output was reviewed and corrected. Comments were categorized by constructs and are used to illustrate the findings.

## 3.2 Short-Term Experiment in a IT and Software Company

3.2.1 *Research Design.* The field study took place from Tuesday, June 11th, 2024, to Thursday, June 19th (see Figure 1), with six candidates split into two groups. Both groups underwent pre-experiment surveys to establish profiles and levels of awareness. They received baseline monitoring for the first two days without feedback, followed by four days of distinct feedback (excluding the weekend). Candidates were recruited via email and in-person discussions in a French IT and software company (Constellation), provided consent forms approved by the company's legal and HR departments, complying with GDPR. Participation was voluntary, including employees of different genders, educational backgrounds, and roles (administrative, commercial, project management, and IT) ranging in age from 25 to 54. Our industry partner anonymized all data before analysis with participants assigned randomly to groups. The

first group (three participants) received real-time historical feedback showing total software energy consumption updated every second, showing consumption for the preceding minute. (Figure 2A), allowing users to observe the energy impact of their actions. The second group (three participants) received real-time disaggregated feedback showing the top 3 consuming software in the past minute (Figure 2B), helping users identify the highest-consuming software.

We depend on a monitoring tool to provide feedback and collect energy data and software usage, called See-PU, derived from the Demeter tool with a focus on CPU and front-end visualization [28]. It measures CPU energy consumption (via a RAPL driver on Windows [7]), the energy and CPU usage of individual processes, and the active window. The monitored data is logged and saved locally in compressed CSV files.

3.2.2 *Data Analysis.* For the experiment, we record the active window every second, capturing the window title, application name, and executable path where needed. We also track each process's CPU usage percentage and energy consumption (in mWh). Additionally, participants completed two surveys. The first contains four sections: user profile (age, gender, socio-economic status), environmental awareness (based on the New Environmental Paradigm [2]), technological competence, and opinions on software energy consumption assessed using a Likert scale. This final section, which assesses the perceived impact of software types in energy consumption, was repeated at the end of the experiment to detect changes due to feedback. Our analysis includes:

(1) Assessing shifts in users' opinions regarding whether software or hardware contributes more to PC energy consumption, along with changes in their perceptions of specific software types (e.g., browsers, productivity tools) during the experiment. Our focus was on determining whether feedback led users to view software energy use as more significant than hardware consumption.

(2) Investigating the relationship between changes in users' perceptions of software energy consumption and their actual usage patterns, including the accuracy of their estimates of
<figure><img src="image_2.png" /><figcaption>Figure 1: Experiment methodology</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 2: Real-time historical (A) and disaggregated (B) feedback</figcaption></figure>

software usage frequency. This analysis examines whether frequent users of specific software types perceive those applications as more energy-intensive and evaluated how precisely users estimated their own usage patterns.

(3) Analyzing user behaviors to understand their interactions with different software types, including usage duration and patterns, and whether feedback on energy consumption influences these behaviors. Our analysis specifically examine whether such feedback prompted users to adopt more energy-efficient software usage habits.

In the next sections, we present and analyze our findings to address RQ1 in Section 4, and RQ2 in Section 5.

# 4 WHAT IS THE END-USERS' PERSPECTIVE ON ENERGY CONSUMPTION IN SOFTWARE?

In this section, we present findings from our study examining end-user perspectives on software energy consumption. Our analysis explores three key dimensions: (1) users' awareness and attention to energy consumption, (2) their understanding of software-related energy use, and (3) their potential behavioral changes to reduce energy consumption. We contextualize these findings through an examination of distinct user profiles and their sociodemographic characteristics.

While 91.5% of our survey respondents claim to know the meaning of energy consumption in computer devices, only 55.3% understand what software energy refers to. Despite this majority, just 51% know how to interpret signs of significant energy consumption during use. Furthermore, although 94.6% of respondents believe it is useful and necessary to monitor energy consumption, only 54.3% actually do so during their usage. Similarly, while 79.3% think individual efforts are crucial for reducing software energy consumption,

<figure><img src="image_4.png" /><figcaption>Figure 3: Respondent answers on energy consumption questions</figcaption></figure>

only 46.8% take action. Thus, our data indicate that awareness of the need does not necessarily lead to concrete actions during usage.

## 4.1 End-users' Perception of Energy Consumption

This section explores the first construct, "Perception," by examining users' awareness and attention to energy consumption in computing devices, and how various factors shape their understanding of the issue. Our study highlights trends in user attention to energy consumption of computing devices, influenced by various factors. Age plays a major role; respondents aged 18 to 24 are the least likely to pay attention to energy consumption, with only 34.06% reporting attentiveness. Tom (names in this paper are changed to protect the anonymity of participants), a 24-year-old literature student, admits, "I don't know what the impact of all that is (...) I need to learn." Lou, a 22-year-old education student, adds, "I have 87 tabs open on Safari (...) I don't think about closing them. It's very vague. I know it consumes energy and all, but in my daily use, I don't think about it." In contrast, 70% of respondents aged 25-34 and around 60% of those aged 35+ report being attentive to their energy consumption. Education levels also matter: respondents with a master's or PhD degree show higher attentiveness (57.5% and 66.1% respectively) compared to other educational levels. Field of study impacts attention as well, with respondents from natural and technological sciences (57.5%) and education (66.5%) being notably attentive. Pierre, a computer science master's holder, shares, "I use a web application to monitor
the consumption of my devices." Moreover, screen time correlates with attentiveness: users estimating 2-4 hours per day are the most attentive, at 64.3%. Finally, environmental sensitivity is the strongest predictor: 74.7% of the most environmentally sensitive users report being attentive, compared to only 33.3% of the least sensitive. Thus, ecological sensitivity strongly drives attentiveness.

## 4.2 End-users' Comprehension of Energy Consumption

This section delves into the second construct, "Comprehension," by investigating users' understanding of the causes, variability, and technical aspects of energy consumption in software, and how this knowledge influences their actions. Nearly 70% of respondents who pay attention to their energy consumption believe they can interpret signs of high consumption, almost 20 percentage points higher than the rest of the sample. Likewise, 72.4% of those who think they understand signs of high energy consumption report being attentive to it during use. This indicates a link between users' attention to energy consumption and their understanding of the concept, referred to as perception. Attention and understanding vary across user profiles. About 45% of respondents do not understand software energy consumption. This gap is particularly notable among those aged 18-24. In contrast, 74.4% of those aged 25-34 and 62.5% of those aged 55-64 are more informed. Consistent with previous results, higher education also correlates with understanding: over 60% of respondents with a master's or PhD degree grasp the concept. Similarly, 73.2% of respondents are aware that energy consumption varies with different software, with minimal variation across profiles. Tom notes, "I think it varies a lot... it has a big impact. (...) It all depends on the software." Respondents typically rank hardware as the most influential factor in energy consumption, followed by software, usage, and peripherals. However, respondents over 35 often rank peripherals second, while more educated respondents place software lower. David, a computer science master's holder, states, "Basically, for me, an application is virtual, it doesn't consume anything. What consumes is the CPU." Despite the concept being vague for many, a majority still acknowledge the impact of usage and software. Lou says, "First, I would say us (the usage), because it is us who use all these tools." Furthermore, although usage and practices mostly rank second or third in our questionnaires, our interviews highlight the pivotal role they occupy in users' perceptions of their energy consumption. Léa, a 21-year-old language student, feels that her battery drains faster when streaming videos than when typing offline. Over 80% of respondents do not know their actual energy consumption, with respondents from natural and technological sciences showing more awareness. Gender also affects responses, with 32.8% of men and 7.7% of women report that they know their energy consumption. When ranking software by energy consumption, entertainment applications top the list, followed by internet browsers and communication tools. This consensus remains despite profile specifics. Most respondents use internet browsers, productivity tools, and communication applications, with a significant portion of computer use driven by work activities. David reflects, "We are driven by our tasks. It's important to understand. (...) Everything I start at a given moment, I don't start for fun. I start because I need it."

## 4.3 Actions

This section addresses the third construct, "Actions," by examining the relationship between users' perceptions of energy consumption and their subsequent behaviors, highlighting key factors that influence their decision to take action. While 79.3% of respondents believe individual efforts are necessary for reducing energy consumption in computing devices, only 46.8% take action during usage, suggesting awareness does not always translate into action. Our data show a correlation between perceptions and actions: 71.1% of those mindful of their energy consumption take action, nearly 30 percentage points higher than average, rising to 84.4% among those fully attentive. Age and education levels also impact action. Only respondents aged 25-34 and 55-64 show a majority taking action (61.5% and 67.5%, respectively), and only those with PhDs report a majority taking action (58.1%), while bachelor's degree holders are least likely (41.7%). In terms of field of study, only those in natural sciences, technology, and education report a majority taking action. Screen time also matters: those with 2-4 hours of screen time per day (59.5%) are more likely to take action than other groups. Finally, environmental sensitivity correlates with action, with higher environmental scores linked to a greater likelihood of taking action. The third and fourth quartiles show a majority taking action, exceeding the first quartile by at least 20 points. Interview data highlight barriers to action, such as lack of knowledge, with 63.5% of respondents reporting insufficient knowledge to act. The qualitative data from interviews help establish a link between users' perceptions and their actions during usage. The need to make energy consumption tangible is highlighted by users like Lise, a 23-years-old social sciences student, who says, "We need to find ways to make it tangible, to realize what it really consumes. (...) Because we don't realize." She adds, "Knowing is super important, I think. (...) Would it make me change my consumption at the moment? I don't know, because it depends on what I'm doing." These findings align with previous results, suggesting that perception is a significant factor in reducing energy consumption. This is especially true for those aged 55-64 (92.3%). Additionally, 19.7% of our sample find changes too restrictive, especially among younger respondents, and those with higher screen time perceive more constraints, indicating that increased screen time exacerbates resistance to action.

## 4.4 Answer to RQ1: what is the end-users' perspective on energy consumption in software?

Our findings indicate a significant gap between awareness and action among end-users regarding software energy consumption. While a majority of respondents are aware of the concept of energy consumption for computing devices, fewer understand it in the context of software. Although many respondents consider that paying attention to energy consumption is important, only about half do so in practice. Furthermore, while a significant portion believes individual efforts are necessary to reduce software energy consumption, less than half actually take action. This highlights that despite a high level of awareness about the importance of energy consumption, this awareness does not necessarily translate into concrete actions among the majority of users.
# 5 CAN FEEDBACK RAISE AWARENESS AND EVENTUALLY REDUCE ENERGY CONSUMPTION?

In this section, we analyze user behavior and the impact of feedback mechanisms on their awareness and actions, through a field study with 6 employees of an IT and software company in France, as described in Section 3.2. The same survey, as with the campus study, was used in the pre-survey, while the post-survey includes additional questions about their perception of the feedback. We detail our findings in the following sections.

## 5.1 Field experiment

Users were asked to rank distinctly, from 1 to 4 (with 1 being the highest impact), what they believed to be the most important variable for energy consumption: software choice, user behavior, PC components, and peripherals. The ranking provided by users partly reflects how responsible they feel for their devices’ energy consumption. Users who rank the latter two variables highly tend to distance themselves from responsibility (thinking the hardware is consuming energy, not their actions), whereas those who rank the former two variables highly believe their choices and actions significantly impact energy consumption. The aim of this ranking is to demonstrate the potential shift in attitudes following a short exposure to feedback on software energy consumption. The results of these questions, by user, are outlined in Table 3 and their profiles is reflected in Table 2. It is important to note that the discrepancy between the number of participants in the experiment (six) and the number analyzed in this sub-section is due to one user (User F) not filling out the exit survey, making it impossible to study the shifts in their perception. Linking back to the campus-wide

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Average change in energy influence factors rankings. With rank 1 being perceived as the most influential and, respectively, rank 4 being perceived as the least influential.</figcaption></figure>

survey in Section 4, we found that age and education were the most significant factors in ranking the influences of ICT energy consumption. The experiment participants fit a limited subgroup of profiles (Table 2), primarily defined by age, education level, and type.

A heat-map of average rankings (Figure 4) shows that candidates with these profiles tend to rank hardware components as the most influential, followed by software choice and usage in second or third place, and peripherals last. This trend is repeated in the experiment, where, prior to feedback, candidates ranked software choice and hardware components as the most impactful, with average scores of 2 each, followed by user behavior (2.4) and peripherals (3.6). After one week of exposure to feedback, software choice rose to the top with a rank of 1.4, user behavior surpassed hardware (2.2 vs. 2.8), and peripherals dropped to 3.8. This shift demonstrates the impact of real-time feedback on user awareness. Although based on a small sample, we can extrapolate that this shift could scale to similar participants.

<figure><img src="image_6.png" /><figcaption>**Figure 5:** Average change in software type rankings. A positive value indicates that users ranked software of the category as having a lesser energy impact than initially thought pre-experiment, while a negative value is perceived as having a bigger impact.</figcaption></figure>

Users ranked eight software categories by energy consumption (1=most intensive): browsers, productivity tools (e.g., office suites), communication (e.g., Zoom), entertainment (e.g., games), utilities (e.g., antivirus), editing/media (e.g., Adobe), development (e.g., IDEs), and other. Campus survey results showed entertainment/media as highest, followed by browsers, then editing tools slightly ahead of communication software. However, experimental data revealed communication and productivity tools as top consumers—a discrepancy explained by participants’ professional usage patterns (age and daily tool reliance). Figure 5 shows evolving perceptions: post-experiment, browsers were viewed as more energy-intensive while communication and productivity tools ranked lower. Given workload-dependent variations, we analyze individual cases rather than aggregate rankings. Entertainment and editing tools were excluded from analysis as they were not work-related for participants, making any opinions based solely on eventual personal use beyond our study’s scope.

5.1.1 *Group 1 - Historical Energy Consumption.* Group 1 received only aggregated CPU energy data per minute, forcing users to identify energy-intensive software themselves. Tables 4 and 5 display
<table><caption>Table 2: Profile of candidates participating to the experiment.</caption><thead><tr><th>Candidate</th><th>Gender</th><th>Age range</th><th>Socio-professional category</th><th>Latest diploma obtained</th><th>Field of education</th><th>Environmental score</th><th>Technological score</th></tr></thead><tbody><tr><td>A</td><td>Female</td><td>35-44 Years old</td><td>Executives and higher intellectual professions</td><td>BTEC Higher National Diploma</td><td>Human and social sciences</td><td>25</td><td>3</td></tr><tr><td>B</td><td>Male</td><td>35-44 Years old</td><td>Executives and higher intellectual professions</td><td>Bachelor's degree</td><td>Natural sciences and technology</td><td>30</td><td>5</td></tr><tr><td>C</td><td>Male</td><td>35-44 Years old</td><td>Executives and higher intellectual professions</td><td>Master's degree</td><td>Natural sciences and technology</td><td>36</td><td>7</td></tr><tr><td>D</td><td>Male</td><td>45-54 Years old</td><td>Executives and higher intellectual professions</td><td>Master's degree</td><td>Natural sciences and technology</td><td>29</td><td>8</td></tr><tr><td>E</td><td>Male</td><td>45-54 Years old</td><td>Executives and higher intellectual professions</td><td>Master's degree</td><td>Natural sciences and technology</td><td>-2</td><td>10</td></tr></tbody></table>

<table><caption>Table 3: Users' ranking of software and hardware contributions to energy consumption - pre and post-experiment</caption><thead><tr><th rowspan="2">Category</th><th colspan="2">A</th><th colspan="2">B</th><th colspan="2">C</th><th colspan="2">D</th><th colspan="2">E</th></tr><tr><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th></tr></thead><tbody><tr><td>Software choice</td><td>3</td><td>1</td><td>2</td><td>2</td><td>2</td><td>1</td><td>3</td><td>1</td><td>2</td><td>2</td></tr><tr><td>User behavior & actions</td><td>2</td><td>2</td><td>3</td><td>1</td><td>1</td><td>2</td><td>1</td><td>2</td><td>4</td><td>4</td></tr><tr><td>PC components</td><td>1</td><td>3</td><td>1</td><td>3</td><td>4</td><td>3</td><td>2</td><td>3</td><td>1</td><td>1</td></tr><tr><td>Peripherals</td><td>4</td><td>4</td><td>4</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4</td><td>3</td><td>3</td></tr></tbody></table>

<table><caption>Table 4: Users' ranking of software type's energy impact - pre and post-experiment</caption><thead><tr><th rowspan="2">Category</th><th colspan="2">A</th><th colspan="2">B</th><th colspan="2">C</th><th colspan="2">D</th><th colspan="2">E</th></tr><tr><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th><th>Pre</th><th>Post</th></tr></thead><tbody><tr><td>Browser</td><td>3</td><td>1</td><td>1</td><td>1</td><td>6</td><td>5</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Productivity tools</td><td>2</td><td>3</td><td>1</td><td>2</td><td>4</td><td>7</td><td>1</td><td>4</td><td>3</td><td>1</td></tr><tr><td>Communication</td><td>1</td><td>4</td><td>2</td><td>1</td><td>5</td><td>6</td><td>1</td><td>1</td><td>2</td><td>3</td></tr><tr><td>Entertainment</td><td>4</td><td>6</td><td>1</td><td>1</td><td>3</td><td>1</td><td>1</td><td>1</td><td>5</td><td>4</td></tr><tr><td>Utilities</td><td>5</td><td>2</td><td>6</td><td>6</td><td>8</td><td>8</td><td>2</td><td>6</td><td>4</td><td>7</td></tr><tr><td>Editing/Media</td><td>6</td><td>7</td><td>1</td><td>1</td><td>1</td><td>2</td><td>1</td><td>1</td><td>8</td><td>8</td></tr><tr><td>Development</td><td>8</td><td>5</td><td>4</td><td>7</td><td>7</td><td>4</td><td>2</td><td>4</td><td>7</td><td>6</td></tr><tr><td>Other</td><td>7</td><td>8</td><td>2</td><td>3</td><td>2</td><td>3</td><td>4</td><td>4</td><td>6</td><td>5</td></tr></tbody></table>

<table><caption>Table 5: Users' software usage frequency</caption><thead><tr><th rowspan="2">Category</th><th colspan="2">A</th><th colspan="2">B</th><th colspan="2">C</th><th colspan="2">D</th><th colspan="2">E</th></tr><tr><th>User</th><th>Real</th><th>User</th><th>Real</th><th>User</th><th>Real</th><th>User</th><th>Real</th><th>User</th><th>Real</th></tr></thead><tbody><tr><td>Browser</td><td>1</td><td>2</td><td>3</td><td>3</td><td>1</td><td>1</td><td>1</td><td>2</td><td>1</td><td>3</td></tr><tr><td>Productivity tools</td><td>3</td><td>3</td><td>1</td><td>1</td><td>6</td><td>2</td><td>1</td><td>2</td><td>3</td><td>1</td></tr><tr><td>Communication</td><td>2</td><td>1</td><td>2</td><td>2</td><td>3</td><td>2</td><td>1</td><td>1</td><td>2</td><td>2</td></tr><tr><td>Entertainment</td><td>6</td><td>5</td><td>4</td><td>8</td><td>2</td><td>8</td><td>8</td><td>8</td><td>5</td><td>8</td></tr><tr><td>Utilities</td><td>6</td><td>3</td><td>8</td><td>5</td><td>8</td><td>3</td><td>8</td><td>3</td><td>4</td><td>5</td></tr><tr><td>Editing/Media</td><td>4</td><td>8</td><td>6</td><td>8</td><td>1</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td></tr><tr><td>Development</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>8</td><td>7</td><td>8</td></tr><tr><td>Other</td><td>7</td><td>4</td><td>7</td><td>4</td><td>7</td><td>3</td><td>8</td><td>3</td><td>6</td><td>4</td></tr></tbody></table>

their pre/post rankings and usage frequency estimates.

User A correctly identified their top three used applications, though browsers showed disproportionately high energy use versus usage time. This led to revised rankings that elevated utilities despite comparable usage. The user understood consumption impacts but requested more detailed (disaggregated) feedback to clarify anomalies.

User B's accurate usage estimates matched consumption patterns, except for productivity tools showing lower-than-expected energy use. Their stable rankings reflected this alignment. Lacking detailed feedback, they cross-referenced task manager CPU data to interpret energy spikes. User C heavily used browsers, communication, and productivity tools but underestimated the latter's usage. Feedback failed to substantially alter their perceptions, partly due to the

coarse one-minute sampling interval limiting detection of consumption patterns. They specifically noted the need for more granular data.

5.1.2 Group 2 - Disaggregated Energy Consumption. Group 2 candidates, though not told which software types were most energy-consuming, could see the top three in real time. User D heavily used communication tools, browsers, and productivity apps, underestimating communication and utilities. Browsers were actually the highest consumers, followed by communication and productivity tools. These appeared 52.8% and 68.8% on the feedback window, respectively, while productivity and utilities appeared rarely (5%, <1%). This shifted User D's perception, seeing productivity and utilities as less consuming but maintaining high awareness of communication and browsing. User E also identified browsers, communication, and productivity tools as top consumers. Despite predicting communication as most used, it proved four times more consuming than browsers and productivity. Feedback showed communication at 76%, browsers at 19.8%, and productivity at 18.4%. Utilities' low visibility (<1%) reduced their perceived impact. User E, though distracted, noticed high consumption (e.g., PowerPoint) but questioned changing behavior. Despite small sample size, users demonstrated greater awareness of energy impacts from their software choices. Disaggregated feedback was more precise (10.50 avg. perception shifts vs. 15.33 in historical feedback). Notably, historical group candidates requested similar breakdowns, with one even using Task Manager to supplement data.

## 5.2 Software Behavior

In this section, we analyze potential shifts in software usage behavior among users, focusing on energy efficiency, closing applications, and the use of alternative software. The objective is to assess how users' interaction with their software and the feedback received influenced their energy consumption and behavior.

<table><caption>Table 6: Closed apps counts - A</caption><thead><tr><th>Software type</th><th>12/06/2024</th><th>14/06/2024</th><th>17/06/2024</th></tr></thead><tbody><tr><td>Browsers</td><td>0</td><td>0</td><td>6</td></tr><tr><td>Communication</td><td>0</td><td>1</td><td>12</td></tr><tr><td>Productivity</td><td>4</td><td>2</td><td>6</td></tr><tr><td>Utilities</td><td>6</td><td>10</td><td>32</td></tr><tr><td>Media</td><td>1</td><td>7</td><td>9</td></tr></tbody></table>

5.2.1 Group 1 - Historical Energy Consumption. For user A from Group 1, the data reveal significant changes in software usage following feedback. Initially, there was an increase in the use of
communication software and browsers, but after receiving feed-
back, user A began closing more instances of these applications
(see Table 6). For example, there were 6 instances of browsing /
navigation softwares being closed on the 17th, 5 of which were
Chrome. It is important to note that the candidate acknowledged
closing browser tabs in response to feedback during the interview.
User A also switched from Microsoft Word to Notepad, likely at-
tempting to use a less resource-intensive alternative, leading to a
drastic reduction in energy consumption from 28.05 mWh to 0.61
mWh for similar tasks of equal duration. This change reflects a
clear attempt to use more energy-efficient software. Despite these
efforts, the overall reduction in energy consumption was modest,
though it demonstrated the candidate's commitment to responding
to the feedback provided.

<figure><img src="image_7.png" /><figcaption>Figure 6: User B active time spent per software type</figcaption></figure>

User B showed a dynamic shift in software usage (as seen in Fig-
ure 6). Communication and productivity tools saw increased usage
after the control period, while browser usage decreased. Peaks in
activity with productivity tools like Excel and Word coincided for
specific days of heightened workload, indicating distinct software
usage patterns. User B expressed frustration over the lack of specific
information on top-consuming applications, which limited their
ability to make more effective changes. Despite this, they reported
efforts to act on the feedback, such as closing inactive Excel pro-
cesses, though this behavior could not be directly verified due to
data granularity (since, as long as an instance of Excel is open, other
closed instances cannot be detected). User C maintained relatively
stable software usage, with a decline in browser usage and consis-
tent use of productivity tools. The candidate reported that the lack
of granular data hindered their ability to act effectively. Despite this,
they managed to reduce Firefox's energy consumption by approx-
imately 50%, which they attributed to adjusting their usage, such
as reducing video playback quality. But it's important to take into
consideration that reduction in energy consumption might also be
influenced by external factors like inactivity or lighter workloads.

5.2.2 *Group 2 - Disaggregated Energy Consumption.* User E demon-
strated consistent usage patterns across software types, with no-
table exceptions in Firefox, which was closed frequently on one day,

potentially due to battery concerns after deeper investigation. Com-
munication software usage peaked at the start of the week, aligning
potentially with routine email checks. User E expressed indifference
torward energy consumption outside of battery-operated devices,
preferring long-term analysis over real-time feedback. They also ar-
gued that the primary focus should be on leveraging IT to optimize
various fields, such as transportation and paper usage. This prefer-
ence suggests that more actionable insights and long-term trends
would be more beneficial for this user. User F exhibited a significant
increase in browser usage and a decrease in system utilities after
the control period. Their focus shifted from developer-oriented
tasks to office tasks, reflecting a change in workload. Despite later
acknowledging the importance of energy consumption and appre-
ciating the feedback tool, User F's energy habits remained largely
unchanged. They suggested that the tool could be improved with
better customization and clearer information, but they felt that its
impact on their behavior was minimal.

## 5.3 Answer to RQ2: can feedback raise awareness and eventually reduce energy consumption?

Our findings indicate that feedback can raise awareness and poten-
tially lead to reduced energy consumption. Initially, users primarily
associated energy consumption with hardware components, but
feedback shifted this perception to include software usage as a sig-
nificant factor. Users were better able to assess the energy impact of
different software types, leading to some changes in their behavior.
For instance, users began to close unused applications and select
less energy-intensive alternatives. However, users' awareness of
energy consumption in software does not always translate into
changes in behavior due to their work-related priorities. While
users recognize the importance of managing energy consumption,
their primary focus remains on completing their tasks efficiently.
They frequently mention that their engagement with software is
driven by necessity rather than choice. Consequently, any inter-
ventions aimed at promoting energy-efficient practices must be
tailored to integrate smoothly into their existing workflows.

# 6 DISCUSSION

The findings of this study reveal a persistent gap between users'
awareness of software energy consumption and their actual be-
havioral changes to reduce it. While our survey showed that 94.6%
of respondents recognized the importance of monitoring energy
consumption, only 54.3% reported actively doing so during usage.
This discrepancy mirrors the "value-action gap" observed in en-
vironmental psychology [21], where intentions frequently fail to
translate to concrete behaviors. To bridge this gap, we propose a
two-pronged approach focusing on user motivation and actionable
guidance.

First, motivating users requires persuasive feedback mechanisms
beyond awareness campaigns. Techniques like gamification, so-
cial comparison [23], and tangible incentives boost engagement,
as shown by participants in our study who received disaggregated
app-level feedback. This aligns with prior work demonstrating
game elements' effectiveness [19]. However, workplace constraints
- where task completion dominates - necessitate non-disruptive
integrations like ambient displays in productivity tools.

Second, our results indicate that even motivated users frequently lack knowledge about concrete actions they can take. The interviews revealed that 63.5% of respondents felt they lacked sufficient information to modify their software usage effectively. This presents an opportunity for the software engineering community to develop practical guidelines and benchmarks. For instance, creating standardized metrics comparing energy efficiency across common software categories (browsers, communication tools, etc.) would empower users to make informed choices. Additionally, our data suggests that users often misattribute energy consumption to hardware rather than software behaviors - a misconception that could be addressed through better interface design. Software developers could incorporate visible energy-saving modes and real-time consumption indicators, similar to the battery saver features now common in mobile operating systems.

The limitations of our study point to valuable directions for future research. While our field experiment showed promising results, the small sample size (n=6) and short duration (one week) warrant larger longitudinal studies. Additionally, the academic and professional demographics of our participants may not fully represent broader populations. Future work should investigate how these findings generalize across different age groups, cultures, and socioeconomic backgrounds. Another promising avenue would be exploring adaptive feedback systems that personalize recommendations based on individual usage patterns and environmental attitudes, as our data showed significant variation in responsiveness based on these factors.

In conclusion, reducing software energy consumption requires addressing both the motivation and capability gaps identified in this study. By combining persuasive feedback mechanisms with clear, actionable guidance tailored to users' contexts, we can move closer to making energy-efficient software usage the norm rather than the exception. This dual approach - leveraging insights from behavioral science while advancing technical solutions - represents a promising path forward for sustainable computing.

# 7 THREATS TO VALIDATION

Our survey, interviews and field studies have a few limitations and threats to their validity.

## 7.1 Campus-wide survey & interviews

Although our study involved around 300 participants, its scope is limited to a single university, which may affect the generalizability across institutions, industries, or regions. While the sample may not fully represent the general population in education settings, it aligns with age and gender demographics, with notable representation of underrepresented groups (e.g., PhD holders). Non-response bias may skew results, as voluntary participation likely reflects certain attitudes or behaviors. Response bias is also a concern, especially with socially desirable answers on sensitive topics, and the forced Likert scale might have compelled respondents to choose a side, though this was intended to increase clarity and engagement. Instrumentation bias could arise from poorly designed or ambiguous questions, and transcription errors may affect qualitative data. Efforts were made to ensure question neutrality and reliability by

reviewing the questions and cross-checking interview transcriptions with recordings. Participant variability adds complexity, as the diverse sample introduces potential biases. To address this, very small or specific demographic groups were excluded from analysis due to limited representation.

## 7.2 Short-term Experiment in an IT and Software Company

The threats to validity in the survey and interviews are similar to those in the previous section and were addressed in the same manner. The study faces several threats to validity that must be acknowledged. First, the small sample size of only six participants in the field experiment limits the statistical significance of the findings. Although the results provide valuable qualitative insights, they should be interpreted as preliminary case study observations rather than conclusive evidence. This underscores the need for future research with larger participant groups to validate and extend these findings. Secondly, selection bias is a significant concern due to participant recruitment via email and in-person discussions, potentially leading to a non-representative sample. However, this was mitigated by a company-wide sustainability initiative that mobilized personnel regardless of personal views, confirmed in post-experiment interviews. Finally, instrumentation threats also exist, particularly with the software used for data logging, but potential failures were minimized through extensive pre-experiment testing and support from our industry partner during the study.

# 8 CONCLUSION

In this study, we combined a campus-wide survey (N=280) and field experiments to examine end-user perspectives and behaviors regarding software energy consumption. Our findings reveal a critical awareness-action gap: while 91.5% of users understood device energy use, only 55.3% recognized software's role, and just 46.8% modified their habits despite 79.3% acknowledging individual responsibility. The research identified significant demographic variations, with older users (25-64) and advanced degree holders demonstrating greater awareness and willingness to change.

Field experiments showed that real-time, disaggregated feedback (highlighting top energy-consuming apps) effectively shifted user perceptions and prompted energy-saving actions, though workplace priorities often limited sustained behavioral changes. While these results demonstrate the potential of user-centric interventions, the study's limited sample size calls for broader validation. Future work should investigate long-term behavioral sustainability through extended interventions that integrate contextual feedback, gamification elements, and personalized recommendations aligned with workflow requirements.

# ACKNOWLEDGMENTS & DATA

The research leading to this publication is supported by the French Research Agency through the ANR-22-CE25-0010 BEHAVE project.

We also thank the Constellation Group for the collaboration over the field experiment. The survey data for the on-campus experiment are available at: https://zenodo.org/records/15124355.
REFERENCES

[1] Icek Ajzen. 1991. The theory of planned behavior. *Organizational Behavior and Human Decision Processes* 50, 2 (1991), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T Theories of Cognitive Self-Regulation.

[2] Mark Anderson. 2012. New Ecological Paradigm (NEP) Scale. *Berkshire Encyclopedia of Sustainability* 6 (01 2012), 260–262.

[3] Luca Ardito, Giuseppe Procaccianti, Marco Torchiano, and Antonio Vetrò. 2015. Understanding green software development: A conceptual framework. *IT Professional* 17, 1 (2015), 44–50. https://doi.org/10.1109/ MITP.2015.16

[4] Tapio Auvinen, Lasse Hakulinen, and Lauri Malmi. 2015. Increasing Students' Awareness of Their Behavior in Online Learning Environments with Visualizations and Achievement Badges. *IEEE Transactions on Learning Technologies* 8, 3 (2015), 261–273. https://doi.org/10.1109/TLT.2015.2441718

[5] Girish Bekaroo, Roopesh Sungkur, Priscilla Ramsamy, Alexander Okolo, and Waseemah Moedeen. 2018. Enhancing awareness on green consumption of electronic devices: The application of Augmented Reality. *Sustainable Energy Technologies and Assessments* 30 (2018), 279–291. https://doi.org/10.1016/j. seta.2018.10.016

[6] Lotfi Belkhir and Ahmed Elmeligi. 2018. Assessing ICT global emissions footprint: Trends to 2040 & recommendations. *Journal of cleaner production* 177 (2018), 448–463.

[7] Howard David, Eugene Gorbatov, Ulf R. Hanebutte, Rahul Khanna, and Christian Le. 2010. RAPL: memory power estimation and capping. In *Proceedings of the 16th ACM/IEEE International Symposium on Low Power Electronics and Design* (Austin, Texas, USA) (ISLPED '10). Association for Computing Machinery, New York, NY, USA, 189–194. https://doi.org/10.1145/1840845.1840883

[8] Jan Dewaters, Susan Powers, and M. Graham. 2007. Developing an energy literacy scale. *ASEE Annual Conference and Exposition, Conference Proceedings* (01 2007).

[9] Thomas Erickson, Ming Li, Younghun Kim, Ajay Deshpande, Sambit Sahu, Tian Chao, Piyawadee Sukaviriya, and Milind Naphade. 2013. The dubuque electricity portal: evaluation of a city-scale residential electricity consumption feedback system. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (Paris, France) (CHI '13). Association for Computing Machinery, New York, NY, USA, 1203–1212. https://doi.org/10.1145/2470654.2466155

[10] Muhammad Umer Farooq, Saif Ur Rehman Khan, and Mirza Omer Beg. 2019. MELTA: A Method Level Energy Estimation Technique for Android Development. In *2019 International Conference on Innovative Computing (ICIC)*. 1–10. https://doi.org/10.1109/ICIC48496.2019.8966712

[11] Nicolas Fournel, Antoine Fraboulet, and Paul Feautrier. 2009. Embedded software energy characterization: Using non-intrusive measures for application source code annotation. *Journal of Embedded Computing* 3 (08 2009). https://doi.org/10.3233/JEC-2009-90

[12] Charlotte Freitag, Mike Berners-Lee, Kelly Widdicks, Bran Knowles, Gordon S. Blair, and Adrian Friday. 2021. The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations. *Patterns* 2, 9 (2021), 100340. https://doi.org/10.1016/j.patter.2021.100340

[13] Erik Jagroep, Jordy Broekman, Jan Martijn E.M. van der Werf, Patricia Lago, Sjaak Brinkkemper, Leen Blom, and Rob van Vliet. 2017. Awakening Awareness on Energy Consumption in Software Engineering. In *2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS)*. 76–85. https://doi.org/10.1109/ICSE-SEIS.2017.10

[14] William Stanley Jevons. 1865. *The Coal Question*. Macmillan & Co. London.

[15] Pasi Karppinen, Harri Oinas-Kukkonen, Tuomas Alahäivälä, Terhi Jokelainen, Anna-Maria Keränen, Tuire Salonurmi, and Markku Savolainen. 2016. Persuasive user experiences of a health Behavior Change Support System: A 12-month study for prevention of metabolic syndrome. *International Journal of Medical Informatics* 96 (2016), 51–61. https://doi.org/10.1016/j.ijmedinf.2016.02.005 Health Behavior Change Support Systems.

[16] Irene Manotas, Christian Bird, Rui Zhang, David Shepherd, Ciera Jaspan, Caitlin Sadowski, Lori Pollock, and James Clause. 2016. An empirical study of practitioners' perspectives on green software engineering. In *Proceedings of the 38th International Conference on Software Engineering* (Austin, Texas) (ICSE '16). Association for Computing Machinery, New York, NY, USA, 237–248. https://doi.org/10.1145/2884781.2884810

[17] Chiraphruet Mansart, Siriluck Sukitphittayanon, Panitan Pantongkhum, and Supphachai Thaicharoen. 2015. Go Run Go: An Android Game-Story Application for Aiding Motivation to Exercise. In *2015 IEEE International Symposium on Multimedia (ISM)*. 407–410. https://doi.org/10.1109/ISM.2015.49

[18] Adel Noureddine, Martín Diéguez Lodeiro, Noëlle Bru, and Richard Chbeir. 2023. The Impact of Green Feedback on Users' Software Usage. *IEEE Transactions on Sustainable Computing* 8, 2 (2023), 280–292. https://doi.org/10.1109/TSUSC.2022.3222631

[19] Brian Orland, Nilam Ram, Dean Lang, Kevin Houser, Nate Kling, and Michael Coccia. 2014. Saving energy in an office environment: A serious game intervention. *Energy and Buildings* 74 (2014), 43–52. https://doi.org/10.1016/j.enbuild.2014.01.036

[20] Candy Pang, Abram Hindle, Bram Adams, and Ahmed E. Hassan. 2016. What Do Programmers Know about Software Energy Consumption? *IEEE Software* 33, 3 (2016), 83–89. https://doi.org/10.1109/MS.2015.83

[21] Antonio Paone and Jean Philippe Bacher. 2018. The impact of building occupant behavior on energy efficiency and methods to influence it: A review of the state of the art. *Energies* 11, 4 (2018). https://doi.org/10.3390/en11040953

[22] Petromil Petkov, Felix Köbler, Marcus Foth, and Helmut Krcmar. 2011. Motivating Domestic Energy Conservation through Comparative, Community-Based Feedback in Mobile and Social Media. In *Proceedings of the 5th International Conference on Communities and Technologies* (Brisbane, Australia) (C&T '11). Association for Computing Machinery, New York, NY, USA, 21–30. https://doi.org/10.1145/2103354.2103358

[23] Petromil Petkov, Felix Köbler, Marcus Foth, and Helmut Krcmar. 2011. Motivating domestic energy conservation through comparative, community-based feedback in mobile and social media. *C and T 2011 - 5th International Conference on Communities and Technologies, Conference Proceedings*, 21–30. https://doi.org/10.1145/2103354.2103358

[24] Clément Pin. 2023. L'entretien semi-directif. https://sciencespo.hal.science/hal-04087897 LIEPP Fiche méthodologique n°3.

[25] Gustavo Pinto and Fernando Castor. 2017. Energy Efficiency: A New Concern For Application Software Developers. *Commun. ACM* 60, 12 (nov 2017), 68–75. https://doi.org/10.1145/3154384

[26] Sergi Rotger-Griful, Rune Hylsberg Jacobsen, Robert S. Brewer, and Mia Kruse Rasmussen. 2017. Green lift: Exploring the demand response potential of elevators in Danish buildings. *Energy Research & Social Science* 32 (2017), 55–64. https://doi.org/10.1016/j.erss.2017.04.011 Energy Consumption in Buildings.

[27] Anders SG Andrae. 2020. New perspectives on internet electricity use in 2030. *Engineering and Applied Science Letter* 3, 2 (2020), 19–31.

[28] Lylian Siffre, Gabriel Breuil, Adel Noureddine, and Renaud Pawlak. 2024. Demeter: An Architecture for Long-Term Monitoring of Software Power Consumption. In *Software Architecture. ECSA 2023 Tracks, Workshops, and Doctoral Symposium*, Bedir Tekinerdoğan, Romina Spalazzese, Hasan Sözer, Silvia Bonfanti, and Danny Weyns (Eds.). Springer Nature Switzerland, Cham, 409–425.

[29] Iana Vassileva, Monica Odlare, Fredrik Wallin, and Erik Dahlquist. 2012. The impact of consumers' feedback preferences on domestic electricity consumption. *Applied Energy* 93 (may 2012), 575–582. https://doi.org/10.1016/j.APENERGY.2011.12.067

[30] Mabel Vazquez, Elitania Jimenez, Juan Ivan Nieto, Juan de Dios Sanchez, Armando Garcia, and Juan Pablo Torres. 2015. Development of a Mobile Health Architecture to Prevent Childhood Obesity. *IEEE Latin America Transactions* 13, 5 (2015), 1520–1527. https://doi.org/10.1109/TLA.2015.7112010

[31] G. Wood and M. Newborough. 2007. Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays. *Energy and Buildings* 39, 4 (apr 2007), 495–503. https://doi.org/10.1016/j.ENBUILD.2006.06.009

Received 10 January 2025; revised 21 March 2025; accepted 21 March 2025