Research on the Impact Mechanism of Self-Quantification on Consumers’ Green Behavioral Innovation

Abstract

The era of self-quantification in green consumption has dawned, encompassing everything from monitoring electricity usage to tracking carbon emissions. By leveraging technological tools to track self-related data pertaining to green behavioral activities, individuals develop self-knowledge and engage in reflection, which in turn influence their participation and even behavioral decisions regarding green activities. Nevertheless, sustainability in consumers’ green behavior is paramount, hinging not merely on participation outcomes but also on behavioral innovation within green activities. Distinct from prior research, this study delves into behavioral preferences transcending mere participation outcomes. It examines the influence of self-quantification on consumers’ green behavioral innovation from the lens of sustainability, elucidating the underlying mechanisms and boundary conditions that govern self-quantification’s effect on the diversity and novelty of consumers’ green behaviors. Drawing on findings from multiple situational experiments, we demonstrate that, in contrast to non-self-quantification, a promotional (defensive) goal orientation tends to diminish (intensify) the innovation of consumers’ behavioral choices. Additionally, consumers’ cognitive flexibility acts as a mediator in the relationship between self-quantification and behavioral innovation, contingent upon their goal orientation. Furthermore, the level of goal concreteness in behavioral activities serves as a moderator, influencing the impact of self-quantification on consumers’ cognitive flexibility under different goal orientations. By investigating the mechanisms through which self-quantification shapes consumers’ green behavioral innovation under varying conditions, this study offers valuable insights for enterprises seeking to guide consumers toward innovative and sustainable green consumption practices.

1. Introduction

With the rapid advancements in network information technology, the proliferation of innovative mobile intelligent terminals, and the widespread adoption of portable sensing devices and numerous real-time monitoring platforms, people’s consumption and living practices have seamlessly integrated numerous digital, data-driven, visual, and traceable elements [1]. By leveraging technological tools like sensors to monitor household electricity consumption and driving-related carbon emissions and utilizing online platforms such as Ant Forest to track green energy credits and green consumption expenditures, individuals are increasingly driven by the concepts of green development and green consumption demands. This trend, coupled with the growing, diverse, and decentralized collection and utilization of quantitative data by technological tools, has led to a heightened data-driven approach in people’s green behavioral activities, marking the dawn of the era of self-quantification in green consumption [2].

Self-quantification involves tracking and measuring data related to one’s own behavior and activities using technological tools or other recording methods. This process fosters self-knowledge and reflection, enabling individuals to intervene and regulate their self-activity participation and even behavioral decision-making [3]. The industry widely acknowledges that, by reflecting on self-behavior through data-driven cognition, self-quantification can empower individuals to maintain healthy behavior initiatives, enhance their beneficial output, and minimize unproductive inputs [4,5]. In the context of green consumption, existing literature underscores that individuals tend to actively engage with quantitative data that describes their behavior, anticipating that self-quantification will facilitate rational regulation and reasonable intervention in their behavioral progress through quantitative self-regulation [6]. For instance, when energy usage data becomes visible, individuals naturally gravitate toward these quantitative metrics and proactively adjust their behavior to favor low-consumption, green energy sources [7,8]. By quantifying their energy-consuming behavior, individuals become more mindful of their energy usage, enabling them to rationally control their consumption and adopt energy-saving practices [9].

However, some scholars have cautioned that the impact of self-quantification is not uniformly positive, and positive outcomes from quantifying negative events can paradoxically lead to reversal effects. For instance, when a user discovers that their energy consumption falls below a set goal limit, they may paradoxically increase their energy usage to meet that limit [10]. In the realm of green consumption, where consumers are the primary actors, they often possess specific goals. Self-quantification, when driven by goal orientation, involves tracking and measuring individual green behaviors and receiving data feedback. This process highlights the disparity between specific goal requirements, limitations, and actual consumer behavior, thereby enhancing consumers’ awareness of goal requirements and the need for goal limitation control [11,12]. When self-quantification is framed as a goal-oriented activity, its effects may vary [13]. Recent research has revealed that in the context of “break and build” green consumption behaviors, differing goal orientations can lead to unexpected outcomes. Specifically, under certain circumstances, self-quantification may diminish the sustained commitment of environmentally conscious consumers to continue protecting the environment and even prompt those who were previously mindful of resource conservation to become more extravagant and wasteful [2].

Regarding green consumption, it encompasses both promotion-oriented activities aimed at enhancing environmental benefits, like tree planting and afforestation, and defense-oriented efforts focused on mitigating environmental harm, such as energy conservation and emission reduction [14]. With the collection and analysis of vast amounts of data, a fresh paradigm for green consumption is slowly taking shape. The industry is increasingly embracing self-quantification as a means to guide consumers in rationally adjusting their behavior across various green activities [10]. However, the current approach, which tends to be outcome-oriented, evaluating the effectiveness of self-quantification based solely on consumers’ behavioral outcomes in both promotion- and defense-oriented green activities [2], overlooks the intrinsic challenges of green consumption. Green consumption is often perceived as more practical than enjoyable, making it challenging for individuals to sustain their involvement under a purely outcome-driven framework [15]. While the outcomes of green consumption are undoubtedly important, the diversity and novelty of participation methods significantly impact individuals’ experiences and their subsequent willingness to continue engaging in green consumption [16]. Consequently, green consumption necessitates innovation from consumers themselves, who must explore innovative ways to engage [17].

Existing research into the varied effects of self-quantification on consumers’ green activity participation outcomes across different contexts is limited in depth, with superficial outcome analyses insufficient to unravel intricate issues like its influence on consumers’ green behavioral cognition and innovative choices [18]. Further investigation is warranted to explore how tracking and measuring one’s own behavior impacts behavioral innovation in green consumption participation, thereby confirming the role of self-quantification in fostering green behavioral innovation. This study aims to comprehensively examine the influence of self-quantification on consumers’ green behavioral innovation within the frameworks of promotional and defensive goal orientations. By considering diverse goal orientations and moderating factors, we seek to elucidate the underlying mechanisms and boundary conditions of self-quantification’s impact on green behavioral innovation across various contexts. Sustainable green participation necessitates novelty and diversity, with consumers’ green behavioral innovation embodying their clear and adaptable understanding of green concepts, as well as their enthusiastic engagement in green activities [16]. The application of self-quantification technology must not only enhance green behavioral participation outcomes in appropriate contexts but also mitigate the risk of mechanical rigidity in consumers’ green consumption endeavors guided by an outcome-oriented mindset. Uncovering the internal mechanisms and effect boundaries of self-quantification on green behavioral innovation will enable green consumption stakeholders to adopt more scientific self-quantification practices and reasonable participation strategies, ultimately fostering innovative and sustainable green consumption patterns.

2. Theoretical Background

2.1. Self-Quantification

Self-quantification is a multi-staged process encompassing tracking, reflection, and subsequent action. Consumers engage in self-quantification for various motivations, including behavioral optimization, self-improvement, self-control and regulation, knowledge exploration, and even self-pleasure [12]. Depending on their motivations, consumers’ participation in self-quantification activities can be categorized into distinct types. When consumers aim to enhance and reinforce positive behavioral patterns through tracking and measuring their activities, they perceive their involvement as positive self-regulation, rendering this form of self-quantification promotion-oriented. Conversely, when the goal is to avoid or mitigate negative behavioral patterns, consumers view their participation as negative self-regulation, making this type of self-quantification defense-oriented [19,20]. Promotion-oriented self-quantification tends to involve more active tracking and measuring (e.g., tracking cycling mileage), whereas defense-oriented self-quantification may involve more passive tracking and measuring (e.g., monitoring electricity consumption) [2]. While the feedback data obtained from self-quantification can influence and even shape consumers’ performance and behavioral choices to some degree in different contexts, their behavioral decisions are also influenced or guided by their goal orientations. The influence of self-quantification on consumer behavior is intricately tied to specific goal orientations [21]. Some research further suggests that self-quantification under different goal orientations can not only facilitate positive behavioral interventions for consumers but also potentially exacerbate irrational behavioral regulation, leading to unintended negative consequences [6,22].

2.2. Green Behavioral Innovation

Green consumption refers to the phenomenon where consumers take into account the environmental impact of their purchasing decisions, striving to minimize negative effects and maximize long-term benefits [23]. When focusing on this phenomenon, existing research has delved into the various factors influencing consumers’ green behavior. These encompass not only individual factors, such as beliefs, values, and attitudes but also social factors, like impression management and group identity [24]. Additionally, the information stimuli encountered by individuals within the consumption environment, for example, behavioral state data from self-quantification tools, can significantly influence their engagement and ultimately shape the outcomes of their green behavior by enhancing their understanding of specific green consumption practices. Contextual factors, like the type of activity and goal orientation, objectively serve as boundary conditions for the transformation of consumer cognition into actual green behavior in the context of self-quantification. These factors play a crucial role in determining the diverse participation outcomes among consumers engaging in green behavioral activities [2].

However, when evaluating and optimizing individual green behavior, it is imperative to consider not merely the outcomes of such behavior but also to thoroughly examine the innovation embodied within it. Innovation serves as a pivotal dimension of behavioral efficacy, not only mirroring an individual’s thinking capacity, problem-solving skills, and adaptability but also influencing the sustainability and reproducibility of the behavior across different contexts [25]. A behavior rich in innovation, despite potentially modest short-term outcomes, possesses immense potential for long-term value [26]. Effective self-quantification tools should strive not only to optimize individual behavioral outcomes but also to foster and empower individuals to integrate innovative elements into their actions, thereby fostering sustained behavioral improvement [27]. Similarly, green consumption necessitates consumer innovation, encouraging consumers to continually mitigate adverse environmental impacts and enhance positive ones through diverse, novel approaches, as opposed to repetitive and monotonous methods [17,28]. Innovation, in comparison to mere participation in green consumption, holds the power to profoundly shape consumers’ experiences, fostering a genuine identification with the values of green consumption and motivating them to actively pursue and adopt a more diverse array of eco-friendly lifestyles. This internal drive fosters a deeper, more enduring experience of green consumption for consumers [29,30].

2.3. Self-Quantification and Green Behavior

With the ubiquitous application of tracking and measurement technology in green consumption, spanning from carbon emissions tracking to electricity consumption monitoring, self-quantification has profoundly altered consumers’ green behaviors. An increasing body of research is delving into and exploring the phenomenon of self-quantification in green consumption, generally forecasting that it will elicit positive behavioral outcomes for consumers [31]. However, scholarly scrutiny has also revealed potential drawbacks, such as overconsumption by individuals who initially aimed to conserve electricity [32] and the cessation of self-quantification among many consumers after brief engagement [33]. To address these negative aspects, existing research has zeroed in on enhancing self-quantification technology models, crafting gamified tracking and measurement schemes to enrich consumers’ experiences and foster positive green consumption outcomes [5]. Nevertheless, related studies infrequently consider the contextual nuances of self-quantification, tending to view green consumption as a single positive concept rather than a dual-faceted phenomenon encompassing both promotional and defensive goal orientations [2]. These studies emphasize the end results of self-quantification, overlooking the behavioral processes involved, and rely heavily on gamification to ensure positive participation outcomes and sustained engagement [6]. Predicting and assessing the sustainability of consumers’ green behavior solely through the promotional goal orientation lens of self-quantification’s positive outcomes is myopic and inaccurate. In contrast, a more comprehensive understanding of whether consumers grasp the green essence of their consumption activities and engage innovatively in green consumption significantly influences the longevity of their green practices [34]. This study delves into the mechanisms underlying self-quantification’s influence on consumers’ green behavioral innovation, shifting the focus from outcomes to behaviors to examine the profound effects of self-quantification. By analyzing the deeper implications of self-quantification on consumers’ green behavioral innovation, as opposed to superficial results, it offers a fresh perspective on the behavioral intervention and optimization process in green consumption fueled by self-quantification.

3. Research Hypotheses

If the inertia behavioral pattern refers to an individual’s tendency to repeatedly make the same choice in a consistent manner during behavioral decision-making, then the innovation behavioral pattern signifies an individual’s tendency to switch between different choices in novel and diverse ways during decision-making, coupled with a strong responsiveness to innovative stimuli [35,36,37]. Essentially, the pursuit of innovation hinges on consumers’ cognitive involvement in behavioral activities, which is intricately linked to their level of cognitive flexibility [38]. Individuals with low cognitive flexibility tend to be more rigid in task execution, adhering to established and singular patterns of thinking during activity participation. This leads to reduced innovation vitality as they focus on task outcomes and adopt a more stable and predictable decision-making path [39,40]. In contrast, individuals with high cognitive flexibility are inclined to challenge conventional norms during task execution. They employ broad and inclusive cognitive categories, actively engage cognitive resources and divergent thinking, maintain high levels of innovation vitality, and participate in behavioral activities through more novel and diverse decision-making paths [41,42,43]. The environmental cue theory posits that the matching of distinct environmental cues with the properties of various activities jointly influences an individual’s cognitive flexibility and, ultimately, their propensity for innovation-seeking. During activity participation, individuals discern the type of environment they are immersed in, subsequently adjusting their cognitive flexibility in response to the environmental cues. This adaptation enables them to pursue behavioral tendencies characterized by diverse innovations, thereby facilitating satisfactory responses to environmental stimuli (Förster et al., 2004 [44]).

Drawing upon this foundation, the present study establishes an analytical framework to delve into the impact mechanism of self-quantification on consumers’ green behavioral innovation (as illustrated in Figure 1). This research model systematically examines the mediating processes and boundary conditions underpinning the influence of self-quantification on consumers’ green behavioral innovation, taking into account various goal orientations.

In the coexistence of “break and build” in green consumption, the activities tracked and measured by self-quantification can be either positively promotional, such as emission reduction activities encouraging consumer engagement, or negatively defensive, involving energy conservation where consumers aim to avoid excessive use [2,45]. Regarding promotional goal-oriented green consumption, activities like purchasing eco-friendly products and adopting low-carbon transportation often encounter limitations in variety and choice, potentially leading to monotonous experiences devoid of novelty and enjoyment [46]. In the context of non-self-quantification, individuals may seek out additional stimuli to evade monotony, thereby fostering diverse and innovative exploration of promotional green consumption behaviors and patterns [29]. Conversely, under self-quantification, the feedback and emphasis on personal promotional green consumption data heightens stimulation, directing individuals’ focus to specific stimuli (e.g., emission reduction) and enhancing attention to the outcomes of their participation [2]. This, in turn, streamlines participation by minimizing distractions, enabling efficient, single-minded engagement in promotional green activities and fostering stable behavioral preferences [6,47]. The intensified focus on promotional goal-oriented green consumption participation facilitated by self-quantification signifies a narrowing of cognitive processing and a decrease in cognitive flexibility [48,49]. This emphasis on outcomes monopolizes limited cognitive resources, fostering rigid cognition and repetitive responses, ultimately leading consumers to prioritize stable, simplistic choices over innovative methods due to cognitive inflexibility. They thus strive for higher promotional green consumption outcomes through repetitive, rather than diverse and novel, behaviors [48,50]. In conclusion, while non-self-quantification consumers, driven by promotional goals, may engage in green consumption with greater cognitive flexibility and a penchant for innovation, those embracing self-quantification tend to exhibit lower cognitive flexibility and adopt less innovative approaches to promotional green consumption.

H1. 

Under the promotional goal orientation, self-quantification will reduce consumers’ green behavioral innovation compared to non-self-quantification.

H2. 

Cognitive flexibility plays a mediating role in the impact of self-quantification on consumers’ green behavioral innovation under the promotional goal orientation.

Regarding defensive goal-oriented green consumption, activities such as conserving electricity and water and promoting a paperless office often leave consumers uncertain about the behavioral outcomes of their energy consumption when not engaging in self-quantification. The act of participating in these defensive green consumption activities frequently imposes psychological pressure on consumers [51]. In situations where perception is uncertain, consumers tend to adopt a more cautious approach to energy consumption decisions, opting for stability and simplicity, thereby engaging in defensive green consumption activities in a relatively conservative manner [52]. In contrast, under self-quantification, firstly, unlike promotional activities, activities centered on defensive green consumption, like tracking energy usage, lead to the acquisition of quantified data that accentuates individual energy consumption outcomes. This intensifies consumers’ psychological pressure and heightens their awareness of the need to restrain their energy consumption behavior. As the sense of restriction on participation grows, consumers seek relief from this pressure through innovative behavior, resulting in more novel and varied choices in their energy usage [36]. Secondly, based on quantified data, individuals perceive a high degree of certainty in their energy consumption and other outcomes from defensive green consumption activities, where the outcomes feel manageable. This fosters a relaxed mindset and encourages consumers to engage in these activities innovatively [19]. For instance, in energy use activities, self-quantification empowers consumers with knowledge and control over their energy consumption progress, broadening their cognitive horizons and allowing them to allocate cognitive resources to other aspects of these activities, like participation methods [53]. The enhancement of individuals’ cognitive processing scope in defensive goal-oriented green consumption participation, facilitated by self-quantification, signifies a heightened cognitive flexibility among consumers. With an expanded cognitive range and broader thinking under self-quantification, consumers make more diverse and novel behavioral choices in defensive green consumption activities [54,55]. Consequently, it is evident that in a defensive goal-orientation, non-self-quantification consumers display lower cognitive flexibility and participate in defensive green consumption with limited innovation, whereas self-quantification consumers, armed with greater cognitive flexibility, actively seek innovation in their participation.

H3. 

Under the defensive goal orientation, self-quantification will enhance consumers’ green behavioral innovation compared to non-self-quantification.

H4. 

Cognitive flexibility plays a mediating role in the impact of self-quantification on consumers’ green behavioral innovation under the defensive goal orientation.

As a long-term and sustained consumption practice, the goals held by consumers in green consumption are often long-term and abstract [56]. Driven by promotional goal-orientation, consumers engaging in green consumption may aspire to “minimize carbon emissions to the fullest extent daily”. However, with the ubiquitous application of tracking and measurement technology, consumers’ goals in promotional green consumption can also become specific, for instance, aiming to “reduce 5 kg of carbon emissions per day”. In promotional contexts, goals varying in their level of concreteness can significantly influence consumers’ ultimate behavioral decisions [57]. Using saving money as an illustration, if an individual establishes a deposit goal as an abstract goal (like saving as much as feasible), they may opt to save from diverse sources. Conversely, if the deposit goal is specific (e.g., depositing a fixed amount), they will likely save from a single, high-yielding source [58]. This is due to the fact that specific goals enhance consumers’ pursuit and commitment to goal attainment, focusing their efforts on the most efficient route to success, thereby solidifying their mindset [59]. A similar dynamic applies to promotional goal-oriented green consumption activities. The ambiguity surrounding participation in promotional green activities under specific goals, stemming from a lack of self-quantification, prompts consumers to overly concentrate on efficient participation strategies with a narrow cognitive processing scope, leading to behavioral choices tailored narrowly for goal achievement. Self-quantification feedback on participation outcomes enables consumers with specific goals to broaden their cognitive processing scope, fostering a higher degree of cognitive flexibility that encourages exploration and experimentation. This, in turn, promotes diverse and novel forms of participation in promotional green activities, marked by greater green behavioral innovation [60,61]. In conclusion, under promotional goal-orientation, consumers’ cognitive flexibility varies according to the concreteness of their goals.

H5. 

The level of goal concreteness moderates the impact of self-quantification on consumers’ cognitive flexibility under promotional goal orientation.

Contrary to the approach tendency of consumers toward promotional goal-oriented green consumption activities, when confronted with a defensive goal-orientation, consumers often exhibit a heightened reluctance to engage in defensive green consumption activities. In terms of goal concreteness, consumers pursuing defensive green consumption may espouse either abstract goals such as “reducing daily electricity consumption” or more specific goals like “limiting daily electricity usage to 10 units”. Using energy consumption as a case in point, within the context of defensive goal-oriented energy usage, an abstract goal can hinder consumers’ ability to precisely delineate the available energy allowance, fostering emotional stress and fostering a cautious mindset. Conversely, more specific goals elicit positive emotions and foster agile thinking in energy-related activities [59,62]. This is because when individuals recognize the boundaries of their participation, they are motivated to assert their autonomy within those constraints; they will awaken a rebellious motivation and express their freedom within the restricted scope, thus employing innovative thinking and even innovative actions [63]. Under specific goals, consumers who can anticipate the available scope of their defensive green activities and perceive ample room for engagement adopt a relaxed attitude, loosening rigid thought patterns and engaging in defensive activities with enhanced cognitive flexibility, thereby making relatively innovative behavioral decisions [36,64]. However, when monitoring and measuring the defensive green consumption process, as exemplified by energy usage, the quantification of progress and self-awareness under specific goals means that each additional energy choice diminishes the available energy allowance. Self-quantification accentuates the energy already consumed, turning each reduction in the allowance into a cautionary signal for consumers, gradually fostering a cautious and rigid mindset during energy-related activities, leading them to adopt safer, more familiar patterns of energy use [65]. In conclusion, the level of cognitive flexibility exhibited by consumers under a defensive goal-orientation varies significantly based on the concreteness of their goals.

H6. 

The level of goal concreteness moderates the impact of self-quantification on consumers’ cognitive flexibility under defensive goal orientation.

4. Experimental Design

Due to practical constraints, existing research has limitations in conducting field experiments in real-world settings to analyze the effects of self-quantification on green consumption behaviors. A select few scholars have ventured into the healthcare field, experimenting with real-world scenarios by equipping consumers with step-counting wristbands [6]. In these experiments, a non-intrusive method was employed to collect step data from non-self-quantification participants through a background program, ensuring their normal walking patterns remained undisturbed. Additionally, factors such as initial activity preference and perceived difficulty were measured and manipulated, offering valuable insights for the experimental design of this study. On the one hand, building upon the classification of green consumption activities proposed by Zhang et al. (2020) [2], which distinguishes between promotional activities that promote sustainable development and defensive activities that hinder it, this study designs experiments centered around carbon emission reduction activities. These promotional goal-oriented green activities serve as the basis for collecting relevant data from consumers. On the other hand, society places great emphasis on water conservation, with water use activities representing a classic example of defensive goal-oriented green activities. Thanks to technological advancements, consumers can now access real-time data on their water usage [66]. Consequently, this study also designs experiments around water use activities, gathering pertinent data from consumers engaged in this defensive goal-oriented green consumption.

4.1. Promotional Goal Orientation Experimental Design

In the first stage, during a weekend in the summer, 80 students from a university in Jiangxi were recruited to participate in a carbon emission reduction activity. These students had an average age of 19.76 years, with 58.75% being male, and they were randomly divided into two groups. The number of students was contingent upon the aggregate seating capacity across the two conversation classrooms. Based on the students’ learning and living environment, and with reference to the “Directions for Quantifying Greenhouse Gas Emission Reduction of Citizens’ Green and Low Carbon Behavior” issued by the local environmental protection department, a total of 14 categories for reducing carbon emissions were provided to the participants for their reference and selection. Prior to the experiment’s commencement, participants were required to scan a QR code on their mobile phones to access a mini-program. The interface of this mini-program displayed 14 distinct categories for reducing carbon emissions along with the corresponding amount of carbon emissions reduced for each category (refer to

Table 1. List of Carbon Emission Reduction Activity Categories.

Activity Name	Reducing Carbon Emissions Value	Activity Name	Reducing Carbon Emissions Value
Reduce disposable tableware usage for 1 time	20 g CO2	Reduce computer usage for 1 h	190 g CO2
Recycle 1 plastic bottle	26 g CO2	Reduce elevator usage for 1 time	218 g CO2
Recycle 1 cardboard box	37 g CO2	Reduce air condition usage for 1 h	621 g CO2
Reduce fluorescent lamp usage for 1 h	41 g CO2	Recycle 1 book	660 g CO2
Reduce electric fan usage for 1 h	45 g CO2	Walk for 1 h	2254 g CO2
Raise a green plant for 1 day	90 g CO2	Recycle 1 old piece of clothing	3600 g CO2
Reduce washing machine usage for 1 h	180 g CO2	Subway travel for 1 h	3736 g CO2

for the list of categories). The precise reduction in carbon emissions for diverse activities could be ascertained by consulting the data outlined in the Carbon Footprint report published by SF Express, a reputable express logistics enterprise in China. After browsing through these options, participants needed to click the “Next” button on the interface, whereupon a prompt appeared stating, “Reducing carbon emissions is a beneficial environmental activity. Please utilize this weekend to minimize your carbon emissions as much as possible”. Subsequently, the mini-program interface presented 14 categories of tags, sorted in descending order based on the amount of carbon emission reduction each represented. The participants were prompted, “You have 48 h to decide which carbon emission reduction activities to engage in. The carbon reduction potential of each activity increases from top to bottom. You may repeatedly select the same category and re-enter the mini-program to click on the tag of the activity category after completing each activity. Tags can be clicked multiple times”. It was emphasized to the participants that the objective of this activity was to evaluate the design experience of the mini-program. Upon completion of the activity, participants were asked to answer several questions. Notably, the extent of their participation in the carbon emission reduction activities did not impact the experimental reward they would receive, but they were mandated to honestly complete the selected activity categories and undergo verification before receiving the reward. Throughout the 48 h period, each participant’s choice of activity categories, the frequency of participation in each category, and the ultimate reduction in carbon emissions were all recorded via the backend of the mini-program.

In the non-self-quantification scenario, every time the participants entered the mini-program and clicked on the completed activity tag, the tag would change from white to green, accompanied by a prompt at the top of the interface stating, “You have completed the X (activity name) carbon emission reduction activity”. Conversely, in the self-quantification scenario, upon clicking the completed activity tag, the same visual change occurred, but the prompt informed the user of their total CO₂ emission reduction: “You have reduced CO₂ emissions by a total of X g”. Following their participation, participants were asked to assess their cognitive flexibility during the activity through the mini-program. Cognitive flexibility was measured using a 5-point Likert scale comprising five items designed to evaluate the extent to which they attempted to break away from rigid thinking patterns. These items included attempting to break through a single way of thinking, considering multiple approaches, exploring diverse solutions, adopting flexible strategies, and engaging in thinking and decision-making processes that deviated from their usual patterns (adapted from Mehta and Zhu, 2015 [36]).

Given the potential interference of relevant factors in participants’ selection of activity categories [4], we also assessed their environmental awareness through a statement like, “Participating in carbon emission reduction activities is beneficial for environmental protection”, rated on a 5-point scale from 1 (completely disagree) to 5 (completely agree). Additionally, we measured their initial level of preference for the activity (“How much did you like carbon emission reduction activities before participating in this study?”), perceived importance (“Participating in this activity is important to me”), and perceived difficulty (“Participating in this activity is difficult for me”), each also rated on a 5-point scale from 1 to 5. Following the collection of these individual differences measures, demographic information related to the participants was recorded. To evaluate the participants’ level of green behavioral innovation, we adopted a method adapted from Etkin (2016) [6]. Specifically, we invited 10 graduate students in environmental science and consumer behavior to comprehensively assess the novelty of each participant’s behavior during the activity participation process. Based on the mini-program data, we used the average number of repetitions in the selected activity category as a proxy for behavioral novelty, considering the number of repeated participations in each category. Furthermore, we analyzed the diversity level of participants’ behavior by examining the total number of different activity categories selected by each participant during the study period. This measure provided an indication of the extent to which participants engaged in a wide range of activities. Finally, the novelty and diversity assessment results were combined to comprehensively evaluate the green behavioral innovation level of each participant.

In the context of promotional goal orientation, statistical analysis revealed no significant differences between the groups of participants in terms of environmental awareness (F(1, 78) = 0.55, p = 0.46), preference for carbon emission reduction activities (F(1, 78) = 0.05, p = 0.82), perceived importance (F(1, 78) = 0.09, p = 0.76), and perceived difficulty (F(1, 78) = 0.18, p = 0.67). These findings excluded the possibility that these factors interfered with the green behavioral innovation of participants engaged in carbon emission reduction activities. Further analysis using an independent samples t-test (as shown in Figure 2) indicated that participants in the self-quantification group generally chose fewer categories of carbon emission reduction activities (M = 7.08, SD = 1.29) compared to those in the non-self-quantification group (M = 9.95, SD = 1.30). Specifically, the categories chosen by the self-quantification group were predominantly high emission reduction activities, whereas the non-self-quantification group selected a mix of both high- and low emission reduction activities. This finding suggests that participants in the self-quantification group exhibited lower diversity-seeking behavior (t(78) = −9.93, p < 0.01). Additionally, participants in the self-quantification group had a higher average number of repetitions of the selected activity category (M = 2.24, SD = 0.35) compared to the non-self-quantification group (M = 1.24, SD = 0.12), indicating lower novelty seeking among the self-quantification group (t(78) = 17.12, p < 0.01). In terms of actual carbon emission reduction, participants in the self-quantification group achieved an average reduction of 18,422.85 g CO₂, while those in the non-self-quantification group achieved an average reduction of 12,415.05 g CO₂. To facilitate comparison, the data were subjected to LN logarithmic transformation, and subsequent analysis revealed that the non-self-quantification group had significantly lower carbon emission reductions (M = 9.34, SD = 0.45) compared to the self-quantification group (M = 9.76, SD = 0.38) (t(78) = 4.56, p < 0.01). These results supported the validation of Hypothesis H1.

In terms of cognitive flexibility, the Cronbach’s alpha value of the test item was 0.96, indicating high internal consistency. Participants in the self-quantification group (M = 2.41, SD = 0.65) reported significantly lower cognitive flexibility compared to those in the non-self-quantification group (M = 3.61, SD = 0.58), as evidenced by a t-test result of t(78) = −8.76, p < 0.01. Regression analysis further revealed that cognitive flexibility positively influenced consumers’ behavioral diversity (β = 1.83, t(78) = 12.20, p < 0.01) and novelty (β = −0.51, t(78) = −10.60, p < 0.01) in promotional goal orientation green activities (because the higher average number of repetitions of the selected activity category implies lower novelty, a negative sign indicates a more positive impact). Following the analysis procedure outlined by Zhao et al. (2010) [67], the bootstrap mediation test method recommended by Preacher et al. (2007) [68] was employed to examine the mediating role of cognitive flexibility. Specifically, Model 4 was selected, with a sample size of 5000 and a bias-corrected nonparametric percentile sampling method used to test for mediation effects. The bootstrap analysis results confirmed that cognitive flexibility mediated the impact of self-quantification on consumers’ green behavioral innovation under promotional goal orientation. The indirect effect of self-quantification on diversity was significant (mean bootstrap estimate = 1.52, SE = 0.28; 95% CI = [1.03, 2.11], excluding 0), indicating that the lower cognitive flexibility reported by the self-quantification group contributed to their reduced behavioral diversity. Similarly, the indirect effect of self-quantification on novelty was also significant (mean bootstrap estimate = −0.22, SE = 0.06; 95% CI = [−0.34, −0.10], excluding 0), suggesting that the same mechanism accounted for their decreased novelty seeking. Based on these findings, Hypothesis H2 was validated, confirming the mediating role of cognitive flexibility in the relationship between self-quantification and green behavioral innovation under promotional goal orientation.

In the second stage, adhering to the experimental design framework established in the first stage, 120 students from a university in Jiangxi were recruited to participate in the experiment. These participants had an average age of 19.73 years, with 53.33% being male. The number of students was contingent upon the aggregate seating capacity across the four conversation classrooms. The experiment employed a 2 (goal concreteness: abstract goal vs. specific goal) by 2 (self-quantification: non-self-quantification vs. self-quantification) between-subjects design, with participants being randomly and evenly assigned to four groups. Prior to the commencement of the experiment, all participants were required to scan a QR code on their mobile phones to access a dedicated mini-program. The interface of this mini-program presented 14 distinct categories aimed at reducing carbon emissions, along with the estimated reduction in carbon emissions achievable through each category (refer to Table 1 for a comprehensive list of these categories). After reviewing the categories, participants were prompted to click the “Next” button, which subsequently displayed a tailored message on the screen. For those in the abstract goal condition, the message read, “Reducing carbon emissions is a valuable environmental endeavor. Kindly strive to minimize your carbon emissions as much as possible over this weekend”. Conversely, participants in the specific goal condition were instructed, “Reducing carbon emissions is a crucial environmental action. Please aim to reduce your carbon emissions by at least 11,023 g CO₂ during this weekend”. Notably, the specific goal threshold of 11,023 g CO₂ was derived from the average carbon emission reduction achieved by 30 participants in the non-self-quantification group under the abstract goal condition during the preliminary phase of the second stage experiment. Subsequently, the experimental procedures outlined in the first stage were followed meticulously to conduct the experiments and gather the necessary data.

In the context of promotional goal orientation, there were no significant differences in the levels of environmental awareness (F(3, 116) = 1.43, p = 0.24), preference for carbon emission reduction (F(3, 116) = 1.54, p = 0.21), perceived importance (F(3, 116) = 1.17, p = 0.33), and perceived difficulty (F(3, 116) = 0.23, p = 0.88) reported among the various groups of participants. This excluded the potential interference of these factors on the green behavioral innovation of participants engaged in carbon emission reduction activities. The results of the independent sample t-test (as shown in Figure 3) revealed that when the given goal was relatively abstract, participants in the self-quantification group generally chose fewer categories of carbon emission reduction activities (M = 7.27, SD = 1.29). Specifically, the selected categories were predominantly those with high emission reduction potential. In contrast, participants in the non-self-quantification group selected more categories of carbon emission reduction activities (M = 10.17, SD = 1.86), covering both high and low emission reduction activities. Participants in the self-quantification group exhibited lower diversity seeking compared to those in the non-self-quantification group (t(58) = −7.03, p < 0.01). Additionally, participants in the self-quantification group had a higher average number of repetitions in the selected activity category (M = 2.22, SD = 0.31), whereas participants in the non-self-quantification group had a lower average number of repetitions (M = 1.23, SD = 0.13). Furthermore, participants in the self-quantification group showed lower novelty seeking compared to those in the non-self-quantification group (t(58) = 16.08, p < 0.01). In terms of actual carbon emission reduction, participants in the self-quantification group achieved an average reduction of 19,361.90 g CO₂, while those in the non-self-quantification group achieved an average reduction of 11,023.37 g CO₂. After performing a logarithmic transformation (LN) on the data, the analysis results indicated that, compared to the self-quantification group (M = 9.81, SD = 0.37), the non-self-quantification group had a lower level of carbon emission reduction (M = 9.24, SD = 0.40) (t(58) = 5.81, p < 0.01). Thus, Hypothesis H1 was validated.

However, when given specific goals, participants in the self-quantification group generally chose more categories of carbon emission reduction activities (M = 10.03, SD = 1.35). In terms of the specific selection of activity categories, the selected categories encompassed both high and low emission reduction activities. In contrast, participants in the non-self-quantification group selected fewer categories of carbon emission reduction activities (M = 7.30, SD = 1.26), with a tendency to choose mostly high emission reduction activity categories. Participants in the self-quantification group exhibited higher diversity seeking compared to those in the non-self-quantification group (t(58) = 8.09, p < 0.01). Additionally, participants in the self-quantification group had a lower average number of repetitions in the selected activity category (M = 1.16, SD = 0.16), whereas participants in the non-self-quantification group had a higher average number of repetitions (M = 2.00, SD = 0.38). Furthermore, participants in the self-quantification group showed higher novelty seeking compared to those in the non-self-quantification group (t(58) = −11.16, p < 0.01). Despite this, in terms of actual carbon emission reduction, participants in the self-quantification group achieved an average reduction of 11,236.47 g CO₂, while those in the non-self-quantification group achieved an average reduction of 20,091.20 g CO₂. After performing a logarithmic transformation (LN) on the data, the analysis results indicated that, compared to the self-quantification group (M = 9.33, SD = 0.02), the non-self-quantification group had higher levels of carbon emission reduction (M = 9.88, SD = 0.25) (t(58) = −11.78, p < 0.01). Nevertheless, it is noteworthy that the participants who engaged in self-quantification ultimately achieved and approached their carbon emission reduction goals. This suggests that while the non-self-quantification group may have achieved greater reductions on average, the self-quantification approach may be more effective in helping individuals meet their individual goals.

In terms of cognitive flexibility, the Cronbach’s alpha value for the test items was 0.97, indicating high internal consistency. When given abstract goals, participants in the self-quantification group (M = 2.39, SD = 0.61) reported significantly lower cognitive flexibility (t(58) = −7.72, p < 0.01) compared to those in the non-self-quantification group (M = 3.57, SD = 0.57). Notably, cognitive flexibility positively influenced consumers’ behavioral diversity (β = 1.26, t(58) = 4.29, p < 0.01) and novelty (β = −0.40, t(58) = −5.81, p < 0.01) in promotional goal orientation green activities (because the higher average number of repetitions of the selected activity category implies lower novelty, a negative sign indicates a more positive impact). In contrast, when given specific goals, participants in the self-quantification group (M = 4.09, SD = 0.72) reported significantly higher cognitive flexibility (t(58) = 9.87, p < 0.01) compared to those in the non-self-quantification group (M = 2.29, SD = 0.69). Here, cognitive flexibility continued to positively affect behavioral diversity (β = 1.00, t(58) = 5.77, p < 0.01) and novelty (β = −0.43, t(58) = −25.90, p < 0.01). The interaction effect between self-quantification and goal concreteness on consumers’ cognitive flexibility was significant (β = −2.98, t(116) = −12.52, p < 0.01), suggesting that the concreteness of goals moderates the relationship between self-quantification and cognitive flexibility. Following Zhao et al.’s (2010) [67] analysis procedure, the bootstrap mediation test method recommended by Preacher et al. (2007) [68] was employed to examine the mediating role of cognitive flexibility and the moderating role of goal concreteness. Model 7 was selected, with a sample size of 5000 and a bias-corrected nonparametric percentile sampling method used to test for mediation effects. The bootstrap analysis results confirmed that the effect of self-quantification on consumers’ green behavioral innovation under promotional goal orientation was mediated by cognitive flexibility, and this mediating pathway was moderated by the level of goal concreteness. Specifically, under abstract goals, the indirect effect of self-quantification was significant for both diversity (mean bootstrap estimate = 1.32, SE = 0.21; 95% CI = [0.93, 1.74], excluding 0) and novelty (mean bootstrap estimate = −0.52, SE = 0.06; 95% CI = [−0.63, −0.41], excluding 0). Under specific goals, the indirect effect of self-quantification was significant for both diversity (mean bootstrap estimate = −1.99, SE = 0.31; 95% CI = [−2.66, −1.42], excluding 0) and novelty (mean bootstrap estimate = 0.78, SE = 0.10; 95% CI = [0.59, 0.99], excluding 0), indicating a shift in the relationship between cognitive flexibility and novelty under different goal conditions. Hypotheses H2 and H5 were thus validated.

4.2. Defensive Goal Orientation Experiment Design

In the first stage, based on the living environment of student dormitories, 10 students from a university in Jiangxi were invited in advance to brainstorm and enumerate the possible water use activities they encounter in their dormitory life. Following the principle of viewpoint saturation, 16 daily water use activities for dormitory life were ultimately identified. Subsequently, on a designated Tuesday in summer (a weekday with no classes scheduled in the afternoon and allowing for dormitory maintenance time), 56 students from the same university were recruited as dormitory units to participate in a water conservation activity (average age 19.34 years, 42.86% male). They were randomly divided into two groups, with 28 students in each group and 4 students per dormitory. The experimental scenarios were standardized for the 4 students in each dormitory. The final number of recruited students was determined based on the principle of the same floor, same major, and balanced gender ratio. Participants were presented with 16 categories of water use activities for reference and selection. Prior to the experiment, participants were required to scan a QR code on their mobile phones to access a mini-program. The mini-program’s interface showcased 16 distinct categories of dormitory water use activities (refer to

Table 2. List of Categories for Water Use Activities in Student Dormitories.

Activity Name	Activity Name	Activity Name	Activity Name
Wash fruits	Take a shower	Mop the floor	Scrub clothes
Wash dishes	Specifically wash hair	Flush toilets	Rinse clothes
Brush teeth	Wash face	Water flowers	Wash duster
Specially wash hands	Wash feet	Wipe tables and chairs	Wipe windows

for the complete list). Upon browsing, participants clicked the “Next” button, whereupon a message appeared: “Saving water is a vital energy-saving measure. Please endeavor to minimize water usage and conserve water resources in your dormitory life for the upcoming day”. Subsequently, the mini-program’s interface displayed 16 various categories of tags, informing participants: “Throughout the next day, after each individual water usage, you must enter the interface and click on the tag corresponding to the activity category completed. Your selections will be recorded accurately and can be repeatedly selected and clicked”. Participants were advised that the aim of this activity was to evaluate the design experience of the mini-program. Following the activity, participants were required to answer a series of questions. Neither the quantity of water used during the activity nor the number of activity categories completed in a single session impacted their experimental reward. However, participants must truthfully complete the selected activity categories and undergo verification prior to receiving their experimental reward. The mini-program’s backend system recorded the number of activity categories completed by each participant during each individual water usage throughout the day, as well as the number of times each category activity was participated in.

The university where the experimental participants resided implemented a card-swiping water mechanism, with a dedicated machine installed on the balcony of the dormitory’s main water pipe. Students were required to swipe their student cards each time they accessed water, enabling the machine to display their individual water usage in real-time and record the total dormitory water consumption annually in the system’s backend. Should the annual usage surpass the university’s predetermined limit, excess charges would apply. This experiment was conducted in June, during which dormitory water usage remained moderate, with ample capacity for additional usage. In the non-self-quantification scenario, the display interface of the card-swiping machine in the participants’ dormitory was obscured with opaque tape, preventing them from viewing their real-time water consumption after swiping. After each individual water usage, participants entered the mini-program where clicking on the activity category tag would turn it green and display a message at the top: “You have completed the X (category name) activity for this water usage”. Under the self-quantification condition, participants were instructed to check their real-time water usage on the uncovered card machine after each swipe. Following water usage, they accessed the mini-program, clicked the relevant activity category tag (which turned green), and entered the amount of water they had consumed. This action prompted a message at the top: “You have accumulated X L of water usage”. This accumulation was automatic, based on each participant’s individual inputs. At the end of the day, participants were asked to report their cognitive flexibility during the activity via the mini-program, using a 5-point scale comprising five items related to creativity and adaptability in water usage (adapted from Mehta and Zhu, 2015 [36]), such as exploring diverse solutions, considering alternative approaches, and employing unconventional water usage strategies.

Given the potential interference of relevant factors in selecting activity categories during water usage [4], participants also provided insights into their environmental awareness (“Participating in water-saving activities is beneficial for environmental protection”, with responses ranging from 1 = Completely Disagree to 5 = Completely Agree), their initial preference for such activities (“How much you loved water-saving activities before participating in this study”, 1 = Very Dislike, 5 = Very Like), perceived importance (“Participating in this activity is important to you”, 1 = Completely Disagree, 5 = Completely Agree), and perceived difficulty (“Participating in this activity is difficult for you”, 1 = Completely Disagree, 5 = Completely Agree). Subsequently, the demographic details of the participants were documented. To assess the green behavioral innovation level of the participants, researchers adhered to Etkin’s (2016) [6] methodology and engaged 10 graduate students specializing in environmental science and consumer behavior to comprehensively evaluate the novelty of participants’ activity participation. This evaluation was based on the average number of activity categories completed by each participant in each individual water usage event, as recorded in the mini-program’s backend. Additionally, the mini-program’s recorded activity participation data were utilized to analyze the total number of activity categories selected by each participant throughout the day for water usage and to assess their diversity in activity participation based on the total number of activity categories completed. These novelty and diversity assessments were then combined to provide a comprehensive evaluation of each participant’s green behavioral innovation level. Meanwhile, the total daily water consumption of each participant was tracked in the card-swiping machine’s backend, allowing for the extraction of specific water usage values.

In the context of defensive goal orientation, no significant differences were observed in environmental awareness (F(1, 54) = 0.49, p = 0.49), preference for energy-saving activities (F(1, 54) = 0.55, p = 0.46), perceived importance (F(1, 54) = 0.09, p = 0.77), or perceived difficulty (F(1, 54) = 1.47, p = 0.23) among the participant groups, thereby excluding the influence of these factors on their green behavioral innovation in water-saving activities. The independent sample t-test results (as shown in Figure 4) revealed that participants in the self-quantification group engaged in a greater number of water use categories (M = 15.36, SD = 2.66), whereas those in the non-self-quantification group participated in fewer (M = 12.00, SD = 1.25). Participants in the self-quantification group exhibited a higher tendency towards diversity seeking compared to those in the non-self-quantification group (t(54) = 6.06, p < 0.01). Additionally, the self-quantification group completed more activity categories per water use on average (M = 2.72, SD = 0.39), whereas the non-self-quantification group completed fewer (M = 1.57, SD = 0.20). Participants in the self-quantification group exhibited a higher tendency toward novelty seeking compared to those in the non-self-quantification group (t(54) = 14.01, p < 0.01). Regarding water usage, the average water consumption of self-quantification participants was 56.54 L, whereas that of non-self-quantification participants was 76.93 L. After applying the LN logarithmic transformation to the data, the analysis indicated that non-self-quantification participants had significantly higher water usage (M = 4.29, SD = 0.35) compared to self-quantification participants (M = 3.99, SD = 0.36) (t(54) = −3.24, p < 0.01). This finding validated Hypothesis H3.

In terms of cognitive flexibility, the Cronbach’s alpha value for the test items was 0.97, indicating high internal consistency. Participants in the self-quantification group (M = 4.54, SD = 0.48) reported significantly higher cognitive flexibility compared to those in the non-self-quantification group (M = 2.79, SD = 0.35) (t(54) = 15.57, p < 0.01). Regression analysis revealed that cognitive flexibility positively influenced consumers’ behavioral diversity (β = 2.22, t(54) = 10.25, p < 0.01) and novelty (β = 0.59, t(54) = 13.46, p < 0.01) in defensive goal orientation green activities. Following Zhao et al.’s (2010) [67] analysis procedure, the bootstrap mediation test method recommended by Preacher et al. (2007) [68] was employed to examine the mediating role of cognitive flexibility. Specifically, Model 4 was selected, with a sample size set to 5000, and a bias-corrected nonparametric percentile sampling method was utilized to test for mediation effects. The bootstrap analysis results confirmed that cognitive flexibility mediated the impact of self-quantification on consumers’ green behavioral innovation under defensive goal orientation. The indirect effect of self-quantification was significant for both diversity (mean bootstrap estimate = −6.23, SE = 0.84; 95% CI = [−8.12, −4.81], excluding 0) and novelty (mean bootstrap estimate = −0.50, SE = 0.20; 95% CI = [−0.94, −0.15], excluding 0). These findings validated Hypothesis H4.

In the second stage, adhering to the experimental design of the first stage, an additional 112 students from the same university were recruited to participate in the experiment (average age: 19.43 years, 46.43% male). The final number of recruited students was determined based on the principle of the same floor, same major, and balanced gender ratio. The study employed a 2 (goal concreteness: abstract goal vs. specific goal) × 2 (self-quantification: non-self-quantification vs. self-quantification) between-subjects design. Participants were randomly assigned to four groups based on their dormitory (4 people per dormitory, 28 people per group). Prior to the experiment, all participants were instructed to scan a QR code on their mobile phones to access a mini-program. The mini-program interface presented 16 types of daily dormitory water usage activities for students (see Table 2 for a comprehensive list). After reviewing the activities, participants clicked the “Next” button, which triggered a prompt on the screen. In the abstract goal scenario, participants were informed that “Saving water is a crucial energy-saving practice. Please strive to minimize water usage and conserve water resources in your dormitory life for the upcoming day”. Participants in the specific goal scenario were instructed, “Saving water is essential for energy conservation. Please aim to use no more than 76 L of water in your dormitory life for the next day”. The specific goal threshold of 76 L was derived from the average daily water consumption of 28 participants in the non-self-quantification group under the abstract goal scenario during the preliminary phase of the second stage experiment. Subsequently, the experimental procedures from the first stage were followed to conduct the experiments and collect data.

In the context of defensive goal orientation, no significant differences were observed among the groups in terms of environmental awareness (F(3, 108) = 0.67, p = 0.58), preference for energy-saving activities (F(3, 108) = 0.08, p = 0.97), perceived importance (F(3, 108) = 0.83, p = 0.48), and perceived difficulty (F(3, 108) = 1.34, p = 0.27). These findings eliminated the potential interference of these factors on the green behavioral innovation exhibited by participants in water-saving activities. The independent sample t-test results, as depicted in Figure 5, indicated that when presented with abstract goals, participants in the self-quantification group engaged in a greater number of water use categories (M = 14.75, SD = 1.71) compared to those in the non-self-quantification group (M = 12.14, SD = 1.24). Specifically, participants in the self-quantification group demonstrated higher diversity seeking (t(54) = 6.53, p < 0.01). Furthermore, they completed more activity categories per water use on average (M = 2.75, SD = 0.39), while those in the non-self-quantification group completed fewer (M = 1.54, SD = 0.18), reflecting a higher level of novelty seeking among the self-quantification participants (t(54) = 14.93, p < 0.01). Regarding water usage, the average water consumption among self-quantification participants was 59.21 L, significantly lower than the 76.04 L consumed by non-self-quantification participants. After applying the LN logarithmic transformation to the data, the analysis confirmed that the self-quantification group used significantly less water (M = 4.02, SD = 0.38) than the non-self-quantification group (M = 4.28, SD = 0.36), with a statistically significant difference (t(54) = −2.58, p < 0.05). Thus, Hypothesis H3 was supported.

However, when presented with specific goals, participants in the self-quantification group generally participated in fewer water-use categories (M = 12.21, SD = 1.32), whereas participants in the non-self-quantification group participated in more water-use categories (M = 13.93, SD = 1.18). The self-quantification group showed lower levels of diversity seeking compared to the non-self-quantification group (t(54) = −5.13, p < 0.01). Furthermore, participants in the self-quantification group completed fewer activity categories per water use on average (M = 1.69, SD = 0.18), whereas those in the non-self-quantification group completed more (M = 2.42, SD = 0.29), indicating lower levels of novelty seeking among the self-quantification participants (t(54) = −11.36, p < 0.01). Regarding water usage, the average water consumption of self-quantification participants was 72.61 L, significantly higher than the 54.14 L consumed by non-self-quantification participants. Upon applying the LN logarithmic transformation to the data, the analysis revealed that the water usage of non-self-quantification participants (M = 3.95, SD = 0.32) was lower compared to that of the self-quantification participants (M = 4.28, SD = 0.10) (t(54) = 5.11, p < 0.01). Notably, despite the increased water usage among self-quantification participants, their final water usage remained close to but did not exceed the maximum water usage goal limitation.

In terms of cognitive flexibility, the Cronbach’s alpha value for the test item was 0.96, indicating high internal consistency. When presented with abstract goals, participants in the self-quantification group (M = 4.29, SD = 0.70) reported significantly higher cognitive flexibility (t(54) = 11.59, p < 0.01) compared to those in the non-self-quantification group (M = 2.49, SD = 0.43). This cognitive flexibility positively influenced consumers’ behavioral diversity (β = 1.40, t(54) = 8.65, p < 0.01) and novelty (β = 0.61, t(54) = 30.38, p < 0.01) in defensive goal-oriented green activities. However, when given specific goals, participants in the self-quantification group (M = 2.51, SD = 0.47) reported significantly lower cognitive flexibility (t(54) = −9.23, p < 0.01) compared to those in the non-self-quantification group (M = 3.89, SD = 0.63). Again, cognitive flexibility positively impacted behavioral diversity (β = 0.94, t(54) = 4.90, p < 0.01) and novelty (β = 0.48, t(54) = 31.19, p < 0.01) in these activities. The interaction effect between self-quantification and goal concreteness on consumers’ cognitive flexibility was significant (β = 3.19, t(108) = 14.75, p < 0.01). Following Zhao et al.’s (2010) [67] analysis procedure, the bootstrap mediation test method recommended by Preacher et al. (2007) [68] was employed to examine the mediating role of cognitive flexibility and the moderating effect of goal concreteness. Specifically, Model 7 was selected, with a sample size of 5000 and a bias-corrected nonparametric percentile sampling method used to test for mediation effects. The bootstrap analysis confirmed that the effect of self-quantification on consumers’ green behavioral innovation under a defensive goal orientation was mediated by cognitive flexibility. This mediating pathway was moderated by the level of goal concreteness. When goals were abstract, the indirect effect of self-quantification was significant for both diversity (mean bootstrap estimate = −2.19, SE = 0.31; 95% CI = [−2.84, −1.63], excluding 0) and novelty (mean bootstrap estimate = −1.00, SE = 0.09; 95% CI = [−1.17, −0.81], excluding 0). Under specific goals, the indirect effect of self-quantification was significant for both diversity (mean bootstrap estimate = 1.67, SE = 0.21; 95% CI = [1.29, 2.11], excluding 0) and novelty (mean bootstrap estimate = 0.76, SE = 0.08; 95% CI = [0.61, 0.91], excluding 0). These results validated Hypotheses H4 and H6.

5. Results

Focusing on the participation behavior in green consumption, this study investigates the influence of self-quantification contextual cues, beyond individual characteristics, on consumers’ green behavioral innovation. The pertinent research findings demonstrate how self-quantification can either diminish or stimulate consumers’ cognitive flexibility under varying conditions, subsequently diminishing or augmenting their behavioral innovation in green consumption practices. Through scenario simulation and field tracking experiments, it has been established that consumers engaging in green consumption activities with different goal orientations have indeed altered their underlying behavioral innovation-seeking tendencies as a result of self-quantification. Specifically:

• Firstly, in promotional goal-oriented green consumption activities, such as emission reduction, when the participation goals are relatively vague and abstract, non-self-quantification consumers tend to select a wider variety of activity categories during the activity period. This results in a lower repetition of activity category selection, indicative of higher green behavioral innovation, albeit potentially leading to a relatively lower final participation outcome. In contrast, consumers who engage in self-quantification tend to select fewer types of activity categories, with a higher degree of repetition in their activity category selection. This pattern reveals lower levels of green behavioral innovation but often translates into a relatively higher participation outcome in the activities.
• Secondly, in promotional goal-oriented green consumption activities, such as emission reduction, when the participation goals are more precise and specific, non-self-quantification consumers tend to select fewer types of activity categories during the activity period. This leads to a higher repetition of activity category selection, indicative of lower green behavioral innovation, yet often resulting in a relatively higher final participation outcome. Conversely, consumers who engage in self-quantification select a wider variety of activity categories, with lower repetition in their activity category selection. This pattern exhibits higher levels of green behavioral innovation but may result in a relatively lower participation outcome in the activities. However, the participation outcome of these self-quantification consumers is still capable of meeting the specified goal requirements.
• Thirdly, in defensive goal-oriented green consumption activities, such as water use, when the participation goals are relatively vague and abstract, during the consumption period, non-self-quantification consumers tend to participate in fewer types of activity categories and complete fewer activity categories with each energy usage, demonstrating lower levels of green behavioral innovation. Consequently, they tend to use relatively more energy for the activity. In contrast, consumers who engage in self-quantification participate in a wider range of activity categories, completing more activity categories with each energy usage and exhibiting higher levels of green behavioral innovation. This ultimately results in the use of relatively less energy for their activities.
• Fourthly, in defensive goal-oriented green consumption activities, such as water use, when the participation goals are more precise and specific, during the consumption period, non-self-quantification consumers tend to participate in more types of activity categories and complete more activity categories with each energy usage, demonstrating higher levels of green behavioral innovation. As a result, they tend to use relatively less energy for the activity. On the other hand, consumers who engage in self-quantification may participate in fewer types of activity categories and complete fewer activity categories with each energy usage, exhibiting lower levels of green behavioral innovation. This can lead to relatively higher energy usage for their activities, although the total energy usage remains within the specified goal limitation.

6. Discussions

6.1. Theoretical Contributions

In terms of research content, this study builds upon existing research findings on self-quantification, cognitive flexibility, and innovation seeking, elucidating the mechanisms and processes through which self-quantification exerts differential effects on consumers’ green behavioral innovation by either diminishing or stimulating consumers’ cognitive flexibility under varying goal orientations. Through a multi-scenario analysis of the effects of self-quantification, we have addressed the limitations of prior studies that tend to solely emphasize the positive aspects of self-quantification [33], thereby enhancing our theoretical understanding of self-quantification. Focusing on the realm of green consumption, this study empirically analyzes and validates the relationship between self-quantification and consumers’ green behavioral innovation. It transcends the outcome-oriented approach of prior research on green consumption [69] and examines green behavioral innovation from a dual perspective of promotion and defense beyond mere participation outcomes. This approach provides insights into understanding consumers’ differentiated green behavioral innovation and even the sustainability of green consumption within diverse self-quantification contexts, offering novel perspectives for future research.

In terms of research perspective, this study expands the exploration of self-quantification effects from the traditional healthcare domain into the realm of green consumption by introducing the concept of self-quantification into consumer behavior literature. By comprehensively assessing the implications of self-quantification across various types of green consumption activities, we transcend the constraints of prior research primarily focused on healthcare and human–computer interaction technology design. This endeavor deepens our comprehension of self-quantification theory from the vantage points of consumer activity participation and behavioral decision-making. Unlike previous studies that analyzed the influence of self-quantification solely on consumers’ participation outcomes in green activities, this research establishes the link between self-quantification and green behavioral innovation. It uncovers the pathway through which self-quantification situations, beyond individual traits, shape consumers’ green behavioral innovation. Importantly, it initially delves into the impact process of self-quantification on consumers’ green behavioral innovation under differing goal orientations. Consequently, this study reveals the extent to which self-quantification influences consumers’ preference for green behavioral innovation, transcending mere participation outcomes in green consumption. Through the validation of relevant mechanisms, it is affirmed that self-quantification not only alters consumers’ participation outcomes in green activities but also enhances the novelty and diversity of approaches adopted to achieve green outcomes.

6.2. Practical Insights

On the one hand, the industry should differentiate between promotional goal-oriented and defensive goal-oriented green consumption activities and apply self-quantification strategies tailored to these distinct goal scenarios. Rather than blindly adhering to trends or implementing self-quantification without understanding its implications, recognizing the varied influence of self-quantification on consumers’ behavioral decisions across different types of green consumption activities will empower the industry to determine whether to guide consumers in self-quantification or create a supportive self-quantification environment, contingent upon the specific activity context. This approach mitigates the potential for detrimental outcomes stemming from the inappropriate application of self-quantification. When designing green consumption activities, the industry must anticipate and quantify both the potential positive effects (e.g., enhancing emissions reduction, diminishing energy consumption, and fostering green behavioral innovation) and negative effects (e.g., diminishing emissions reduction, enhancing energy consumption, and hindering green behavioral innovation) that self-quantification may have on consumers participating in these activities. This assessment should consider the nature of the activity’s goal orientation and the level of goal concreteness. Following a careful weighing of these pros and cons, the industry should make an informed decision regarding the application of self-quantification, aligned with its value propositions, and devise green consumption activities that effectively guide consumers’ behavioral choices.

On the other hand, by comprehending the impact of self-quantification on consumers’ green behavioral innovation within diverse goal contexts, we can empower consumers with a clearer understanding of self-quantification’s effectiveness. Additionally, this understanding aids the industry in leveraging self-quantification more judiciously to refine green activity design, steer consumers’ green behavioral innovation preferences, and ultimately foster the sustainability of green consumption. Given the differentiated influence of self-quantification on consumer innovation across goal contexts, the industry should devise and design green consumption activities, as well as offer green products and services that cater to varying goal orientations (such as promotional and defensive) or the perceived concreteness of goals within the same goal-oriented green activities (abstract versus specific goals). For instance, when aiming to motivate consumers to engage in carbon emission reduction activities characterized by high innovation potential and compliance, precise and achievable consumption goals should be established for participants in green consumption initiatives, accompanied by quantitative data feedback throughout their involvement. Conversely, when encouraging minimal energy consumption with similar innovation vitality, if tracking and measuring energy usage behavior is deemed necessary, the energy consumption constraints should be framed as vague and general. Alternatively, if the set energy consumption limit is precise and specific, it is advisable to refrain from providing self-quantification conditions to consumers within these energy consumption activities, as it may hinder the intended outcomes.

6.3. Research Limitations and Future Research Directions

In addition to the types of activities and the concreteness of goals within goal orientations, other factors may also modulate or interfere with the mechanism and process through which self-quantification influences consumers’ green behavioral innovation. Prior literature indicates that the presence or absence of others during activity participation, whether in public or private settings, can exert a differential moderating effect on individual innovation performance. For promotional goal-oriented activities, in comparison to private situations, public contexts may prompt consumers to receive positive evaluations from others for their diverse and novel behavioral choices. This, in turn, can foster perceptions of their innovation and interest among others, potentially enhancing their drive for innovation. Conversely, in the case of defensive goal-oriented activities, public situations may lead to negative evaluations from others if consumers engage in hedonic activities in unconventional or novel ways. The pressure associated with such diverse and novel choices may diminish their motivation to seek innovation [69]. With the proliferation of social networking platforms, consumers’ green behavioral activities have become increasingly public, shared, and subject to comparison, transforming them into collective endeavors. Consequently, further research is imperative to investigate whether the impact of self-quantification on consumers’ green behavioral innovation varies depending on whether the activity context is public or private. Furthermore, the study prudently chose dormitories as miniature living spaces to carry out field experiments. The established defensive green consumption activities encompassed a comprehensive range of water usage activities that typically occurred within dormitory life, encompassing clothing laundering, hygiene cleaning, washing, and watering plants. Nevertheless, it is acknowledged that there exist minor variations between dormitory life and the daily lives of residents. Consequently, future endeavors ought to broaden their scope by incorporating ordinary residents as research participants. This would entail gathering data on their daily water consumption patterns for detailed analysis while also accounting for the diverse personal preferences and behavioral proclivities that individuals may exhibit towards various water-related activities, such as those mentioned above. By doing so, the research can aspire to attain an even greater degree of generalizability and applicability in its findings.