Can Heuristic Cues Improve Voluntary Carbon Offsetting Message Effectiveness?

Abstract

Encouraging air travelers’ participation in voluntary carbon offsetting (VCO) remains challenging. Drawing on dual-process and social influence theories, this study investigates whether heuristic cues can optimize message design for online carbon offsetting. Through sequentially designed randomized national surveys and psychophysiological experiments, results revealed that messages leveraging primacy-recency, anchoring, and foot-in-the-door techniques are most effective in increasing carbon offsetting intentions. Findings indicated that women tend to be rational carbon offset purchasers while older men are more likely to be heuristic-induced purchasers. The experimental study further reveals that transparency and efficacy messages with statistical information, rather than heuristic cues, are the most effective approaches in raising respondents’ attention to carbon offsetting details. Practical communication strategies such as providing readable and accurate information are proposed to promote participation in aviation VCO programs.

Keywords

heuristic cues carbon offsets climate change heuristic-systematic mode air travel message design

Introduction

Tourism is responsible for 8% of global carbon emissions (Lenzen et al., 2018), over 40% of which is caused by air travel (Gössling, 2009). According to the European Federation for Transport and Environment (2016), demand for air travel will increase from 2.4 billion in 2010 to 16 billion in 2050, resulting in a tripling of carbon emissions in a time window of only 30 years. Over 60% of the carbon budget to keep global warming below 2°C has already been emitted (Gössling et al., 2023; Rogelj et al., 2016). If aviation emissions are not effectively mitigated, the long-term goal of preventing climate change will become unachievable (Becken & Mackey, 2017).

Under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA), many airlines have developed voluntary carbon offset schemes to achieve the target of “carbon-neutral growth” from 2020 onward (International Civil Aviation Organisation [ICAO], 2017). Voluntary carbon offsetting (VCO) is the purchase of carbon credits to compensate for one’s carbon footprint over and above mandatory requirements (Jou & Chen, 2015). The program helps raise public awareness of aviation’s environmental impacts and allows air travelers to mitigate their flight carbon footprints by directly investing in environmental projects (Broderick, 2009). Airlines adopt various strategies to encourage VCO, such as offering purchasing in online booking systems, using member mileage points to offset carbon emissions, and providing information on involved environmental projects on websites (Mair, 2011). However, after more than 10 years of promoting carbon offset schemes, uptake remains low—between 2% and 10% (Choi et al., 2016; Ritchie et al., 2019).

Several factors explain this low uptake, including low awareness of and limited knowledge about aviation carbon offsets by the public (e.g., Gössling et al., 2009; Y. Kim et al., 2018) and the lack of credible and transparent offsetting schemes (e.g., Babakhani et al., 2017; Zhang et al., 2019b). People’s willingness to purchase carbon offsets is also impacted by psychological and contextual factors, such as environmental attitudes (Lu & Wang, 2018), values (Mair, 2011), beliefs (Choi et al., 2016), desires (Y. Kim et al., 2018), social norms (Araghi et al., 2014), and global policy knowledge (Ritchie et al., 2019). The way offsets are communicated represents a significant potential leverage point to increase uptake by improving increasing perceived credibility (e.g., Zhang et al., 2019a), awareness of (e.g., Babakhani et al., 2017) and knowledge about carbon offsetting schemes (Y. Kim et al., 2018). However, existing studies predominantly assume that air travelers engage in rational decision-making, overlooking the role of heuristic processing in consumer purchase behaviors (Song et al., 2024).

According to dual-process theories, people process information, evaluate situations and make decisions in two distinct ways: via heuristic processing (i.e., less cognitive effort, low consciousness, and involvement) or systematic processing (i.e., comprehensive evaluation, high consciousness, and involvement; Kahneman, 2011). When a task is less critical, risky, or complicated, individuals deploy mental shortcuts (heuristics) to save time, and energy (Fiske & Taylor, 1991). Since voluntary carbon offsets are typically purchased in busy online booking interfaces, air travelers may use heuristics to simplify their purchase decisions. The unpredictable effect of purchasing carbon offsets further increases the likelihood of consumers relying on heuristics (Tversky & Kahneman, 1974). Although heuristic cues such as anchoring and primacy-recency effects have been shown to influence consumer decision-making (Nazlan et al., 2024), their effectiveness in the VCO domain has seldom been empirically examined. Moreover, there is little discussion on how personal attributes (e.g., cognitive styles, prior experiences, sociodemographics) influence their processing of VCO messages.

Given the complexity of visual engagement in online booking platforms, identifying the message elements that effectively attract and retain attention is vital for designing more persuasive communications. Prior research suggests that numerical transparency, message framing, and visual placement may influence how individuals process information online (Babakhani et al., 2017; Zhang et al., 2019b). However, limited research examined which specific message attributes most effectively capture and maintain travelers’ attention to VCO information. Furthermore, existing research has relied on self-reported questionnaires to assess the effectiveness of carbon offsetting messages (Ritchie et al., 2021), with few studies incorporating psychophysiological measures to analyze respondents’ attention to information. Whether travelers actively attend to systematic or heuristic carbon offsetting information on airline booking websites remains unclear.

To address the knowledge gap, the present study aims to investigate the effectiveness of heuristic VCO messages in improving air travelers’ awareness and purchase. Specifically, we ask (1) whether heuristic cues increase message effectiveness of airline VCO; (2) which factors influence air travelers’ information processing styles when exposed to VCO messages; (3) how systematic-heuristic information processing of VCO messages affect purchase intention; and (4) what are the important attributes in raising people’s attention to VCO messages?

This study contributes to existing knowledge by examining whether heuristic cues are effective in carbon-offsetting communication. By combining quantitative survey methods and experimental approaches, the research identifies the optimal communication message attributes that can raise public attention and purchase intention. Furthermore, the study highlights individual differences in applying systematic and heuristic thinking in response to carbon-offsetting messages.

Findings have practical implications for airlines; they guide them in designing carbon-offsetting communications to increase uptake. With millions of online bookings each year, even a small increase in purchasing aviation carbon offsets could significantly impact and help mitigate emissions from aviation.

Literature Review

Heuristic Cues as Nudges

Heuristic cues refer to “simple, efficient rules that guide individuals’ judgments and influence decision-making processes” (Gilovich et al., 2002, p. 723). Heuristics are mental shortcuts that cause cognitive bias and lead to fast decisions by consumers. Heuristics can be used to nudge behavior, representing an alternative approach to providing information to change behavior via increased knowledge and awareness (Hickson & Khemka, 2014). Heuristic cues can be divided into representativeness, anchoring and availability heuristics (Tversky & Kahneman, 1974). In online purchasing, research has focused on the impact of heuristics on consumer reviews (Hlee et al., 2018; Nazlan et al., 2018); encouraging carbon offsetting has largely been neglected. Given that VCO is a prosocial behavior similar to charitable giving, social heuristics hold substantial promise as potentially powerful behavior change strategies (Kraft-Todd et al., 2015).

Social heuristics guide people’s decisions in social environments (Rand et al., 2014). Although most studies assume that prosocial behaviors are triggered by intrinsic motivation and personal norms (e.g., Han, 2015; Joanes, 2019), some argue that moral behaviors are primarily unconscious and driven by social heuristics (Gigerenzer, 2010; Haidt & Bjorklund, 2008). For instance, people tend to imitate the majority or comply to gain group acceptance (Keltner et al., 2008). Based on social influence theory (Kelman, 1958), multiple techniques have proven successful in increasing cooperative behaviors, such as the Disrupt-Then-Reframe (Davis & Knowles, 1999), Legitimization of Paltry Favors (Cialdini & Schroeder, 1976), Foot-in-the-Door (Freedman & Fraser, 1966), Door in the Face (Cialdini et al., 1975), and But You Are Free (Gueguen & Pascual, 2005). Since social influence techniques allow people to use social information to make simple decisions, this study includes them as social heuristics cues.

Anchoring heuristic refers to people’s tendency to estimate values by starting from an “initial value” and adjusting to that value (Tversky & Kahneman, 1974). Price anchoring significantly impacts price evaluations and purchase behaviors (Book et al., 2016; Hüsler et al., 2013); people are more willing to pay when provided with a high versus a low price anchor (e.g., Tanford et al., 2019).

The availability heuristic is defined as the tendency to evaluate the frequency or probabilities of an event by how easily that event can be recalled (Kliger & Kudryavtsev, 2010). Given people’s limited cognitive processing ability, readily available information impacts decision-making (Kahneman, 2011). Factors such as primacy-recency, vividness, salience, and negativity can make information more accessible, significantly improving persuasion effectiveness (Nazlan et al., 2018). Primacy-recency heuristic cues highlight the salient issue of carbon emissions, which helps to encourage VCO purchases.

Nudging VCO Behaviour

The extant studies found that public engagement in purchasing VCO is low (e.g., Gössling et al., 2009; Mair, 2011). Research into increasing demand for VCO purchases aligns with three streams: (1) exploring the psychological and contextual factors that affect purchase intention or willingness to pay (e.g., Ritchie et al., 2019); (2) identifying personal characteristics of people who purchase carbon offsets (e.g., McLennan et al., 2014; Ritchie et al., 2021); and (3) developing and testing communication approaches that maximize uptake (e.g., Babakhani et al., 2017; Zhang et al., 2019a).

Based on value-belief-norm theory (Schwartz, 1977) and the theory of planned behavior (Ajzen, 1991), the first group of studies stated that air travelers’ attitudes (e.g., Choi et al., 2016; Lu & Wang, 2018), environmental values (Davison et al., 2014; Gössling et al., 2009), personal norms (Chen, 2013), social norms (Choi et al., 2016), and desires (Y. Kim et al., 2018) significantly increase their intention to purchase carbon offset. These studies investigated self-reported behavioral intentions rather than actual behavior. Based on random utility theory (Manski, 1977), factors including knowledge, effectiveness and offset awareness are the strongest drivers of people’s willingness to pay for carbon offsetting (e.g., Lu & Shon, 2012). Several meta-analyses show that the intention-behavior correlations rarely exceed 0.56 (Sheeran, 2002), suggesting many carbon offset interventions may be ineffective. Because VCO is a behavior unlikely to be recognized or constrained by other people, air travelers may not pay carbon offsets even if they intend to or are willing to pay.

The second stream of studies explored the segments and profiles of carbon offset purchasers. Most carbon-offsetting purchasers are domestic rather than international air travelers (Choi et al., 2018; Choi & Ritchie, 2014). Moreover, European travelers were more likely to make voluntary carbon offset payments than Asian tourists, which highlights the effectiveness of social marketing on a macro level (McLennan et al., 2014). People who are young, work full-time and live with children are more likely to offset their flights (e.g., Mair, 2011; Ritchie et al., 2021). Although pro-environmental people may be higher earners and well-educated tourists, the understanding of actual environmental-friendly travelers is minimal (Dolnicar et al., 2008).

The third group of studies emphasized the importance of communication interventions in encouraging carbon-offsetting purchases. Providing an environmental label instead of calculating carbon credits to recognize air travelers’ contribution has proven more effective in persuading their intention to purchase (Gössling & Buckley, 2016; Liu et al., 2015). Using psychophysiological measures, pictorial and short textual information can effectively raise people’s attention toward carbon offsetting messages (Babakhani et al., 2017). Studies also attempted to use nudge-style interventions, including the anchoring effect (Székely et al., 2016; Tanford et al., 2019) and pro-self/-social nudge (Tyers, 2018) to change tourists’ purchase behaviors. However, how heuristic interventions can encourage air travelers to purchase carbon offsets is unknown. Further, air travelers are regarded as rational carbon-offsetting purchasers, which fails to identify individual differences in information processing and decision-making. Thus, we assume that heuristic interventions, systematic-heuristic processing, and sociodemographic factors affect carbon offset purchase intention.

Heuristic-Systematic Information Processing Toward Carbon Offsetting

Dual-process models, including the elaboration likelihood model (Petty & Cacioppo, 1986) and the heuristic-systematic model (Chaiken, 1987), have been widely used to explain how people’s information processing guides their judgment and decisions. The core assumption of both models is that people engage in either heuristic (System 1) and/or systematic (System 2) information processing. Heuristic processing requires fewer cognitive resources and uses simple decision rules (Ryu & Kim, 2015). Systematic processing involves effortful and rational thinking, which requires careful consideration of object-related information (Gawronski & Creighton, 2013). While the elaboration likelihood model regards dual processes as an inverse relationship between central and peripheral processing, the heuristic-systematic model proposes that people can simultaneously engage in both processing models (Y. Kim et al., 2018).

Systematic processing demands considerable cognitive resources, invoking careful, and rational contemplation of pertinent information (Gawronski & Creighton, 2013). Characterized as deliberate, effortful, and rational thinking, it involves people’s logical reasoning, and evidence-providing toward pro-environmental issues. Research indicates that air travelers will likely seek comprehensive information before making carbon-offsetting decisions. This includes acquiring knowledge of environmental impacts, ensuring transparency of information provided, providing personal choices, assessing the effectiveness of offset programs, considering prevailing norms regarding VCO purchase, and evaluating the cost-benefit tradeoffs (e.g., Denton et al., 2020; Y. Kim et al., 2018; Polonsky et al., 2011).

Heuristic processing is an economical cognitive method that involves minimal cognitive effort and the utilization of simple rules to make decisions (Ryu & Kim, 2015). When faced with tasks perceived as less critical, hazardous, or pertinent, individuals displaying low motivation may seek cognitive heuristics to decrease personal efforts (Kang & Sung, 2022). Given the complex nature of the online booking process, it is common for people to be nudged when making purchase decisions using heuristic approaches (Hlee et al., 2018). To explore the effectivess of heuristic processing, various nudging cues have been examined in prosocial studies, such as Disrupt-Then-Reframe, Legitimization of Paltry Favors, Foot in the Door, Door in the Face, Imitate the majority, But You Are Free, Anchoring, and Primacy-recency cues (See Table 1). Considering the limited research on heuristic cues within carbon offsetting, there is a need to investigate air travelers’ perceptions of systematic and heuristic information concerning online VCO purchases.

Table 1.

Systematic-heuristics Information Process of VCO Messages.

Information processing	Details	References
Systematic information
Knowledge	Relevant information and facts about carbon offsetting	Denton et al. (2020)
Transparency	Transparent reports and declaration of carbon-offsetting activities	Delacote et al. (2024)
Choice	Options for VCO purchase	Choi et al. (2018)
Efficacy	Active effectiveness in reducing carbon emissions	Wang et al. (2024)
Social norm	Public expectation and moral norms	Araghi et al. (2014)
Cost/benefit	Personal and collective interests	McArthur (2020)
Heuristic information
Disrupt-Then-Reframe	Confuse briefly, then clarify with an appeal to gain compliance	Allen and Allen (2016)
Legitimization of Paltry Favors	Small contributions framed as significant	Guéguen (2013)
But You Are Free	Offering choice enhances agreement	Gueguen and Pascual (2005)
Foot in the Door	Initial minor consent leads to a larger agreement	Gamian-Wilk and Dolinski (2020)
Door in the Face	The large initial request sets up smaller ask’s success	Pascual and Gueguen (2006)
Anchoring	The initial number influences the final decision	H. L. Kim and Hyun (2021)
Imitate the majority	Copying others encourages compliance	Nazlan et al. (2018)
Primacy-recency	The first and last information most influential	Wattanacharoensil and La-ornual (2019)

Research Design

Australia was chosen as the study context due to its high public awareness of carbon offsetting, which ensures reliable public understanding and responses to VCO messages. As a multicultural and immigrant-rich nation, Australia offers a diverse sample encompassing cross-cultural dynamics. The research employed a sequential mixed-methods approach, which involved a quantitative survey, experimental study, and qualitative research to enhance the credibility and reliability of the findings. As shown in Figure 1, drawing from the literature review, Study 1 is a national survey conducted to examine the crucial factors of VCO systematic and heuristic information. In Study 2, an eye-tracking laboratory experiment with interviews was launched to explore the effectiveness of designed information in raising respondents’ attention to VCO messages (Figure 1).

Figure 1.

Design of the research.

Study 1: Survey Research

Design and Procedure

The research is based on a national survey. The questions in the survey consist of four main sections, which aim to investigate respondents’ (1) demographic information, (2) frequency of online airline ticket purchase (ranging from 1 = once to 21 = over 20 times) and past carbon offsetting purchase in 2019 (1 = Never; 2 = Yes, for domestic flight; 3 = Yes, for international flights; 4 = Yes, for both domestic and international flights); (3) perceived persuasion by VCO messages; and (4) heuristic and systematic processing modes of carbon offsetting information. After completing sections 1 and 2, participants were randomly presented with one of nine message stimuli about VCO and rated the perceived persuasiveness of the message. Ten items were selected to measure respondents’ heuristic and systematic processing modes on a seven-point agreement scale ranging from 1 = strongly disagree to 7 = strongly agree.

To maximize external validity, the nine message stimuli were designed by simulating carbon offsetting messages by modifying an airline booking website. Respondents were randomly assigned to one control group and eight manipulated experimental groups. In the control group, participants saw the default message describing basic information about VCO, such as “Carbon offsets are used to fund accredited initiatives which counteract carbon emissions”. In the experimental conditions, respondents saw additional text using different heuristic cues in the second paragraph of the message. Messages were similar in length, ranging from 18 to 25 words. The heuristic cues in the added text included (1) “Disrupt-Then-Frame,” (2) “Legitimization of Paltry Favors,” (3) “But You Are Free,” (4) “Foot in the Door,” (5) “Door in the Face,” (6) “Anchoring,” (7) “Imitate the Majority,” and (8) “Primacy-Recency” techniques (see details in Figure 2 and Table 2). After respondents saw the message, they rated its effectiveness in convincing them to purchase VCO (scale from 0 = not effective at all to 100 = highly effective). To measure respondents’ perceived effectiveness in collective persuasion, respondents were asked to estimate the percentage of air travelers purchasing VCO when seeing this message.

Figure 2.

Examples of the treatment message: (a) control group and (b) experimental group with “Disrupt-Then-Reframe” message.

Table 2.

Experimental Groups.

Experimental group	Message	Heuristic cue
1	Don’t think of it as supporting projects. Think of it as paying your fair share. Every kilometer you fly causes 370 kg in carbon emissions.	Disrupt-Then-Reframe (social heuristic)
2	No amount of money is too small to make a big difference. Every cent counts when it comes to keeping those carbon offset projects going.	Legitimization of Paltry Favors (social heuristic)
3	This is entirely your choice. Fly Carbon Neutral is a voluntary program. You are free to choose to make or not to make a contribution.	But You Are Free (social heuristic)
4	It is very easy and affordable to make small carbon offset contributions: a few cents, a few dollars.	Foot in the Door (social heuristic)
5	Emissions have increased 300% since 1990. You should contribute $60 per flight. But that’s a big ask. You can start by offsetting this flight only.	Door in the Face (social heuristic)
6	Carbon offset contributions for flights vary between $1 and $26 depending on how long the flight is. The average offset costs about $7.	Anchoring (anchoring heuristic)
7	More air passengers want to offset their carbon emissions. Over half want offsetting to be included automatically when they book. We believe it should be voluntary.	Imitate the majority (social heuristic)
8	The threat is very real. Just remember the recent bushfires that killed many Australians and ruined the lives of many more.	Primacy-Recency (availability heuristic)

Data Collection

An online nationwide questionnaire was administered using Qualtrics and collected by an Australian market research company across 3 months. We recruited 15 university students in the first survey round and 14 tourism academics in the second. Respondents examined the questionnaire and assisted in refining materials. Upon finalization of the questionnaire, we surveyed adult Australian residents. We used a quota sampling approach with quotas set for gender, age, education, and place of residence based on national population statistics (Australia Bureau of Statistics, 2016). Only respondents who have booked a flight online in the past 12 months were recruited for the research. We included two attention-check questions (e.g., “Please choose ‘strongly disagree’ below”) throughout the survey to ensure attentiveness. Responses that failed these attention checks were excluded from the analysis, thereby improving the reliability of the data collected. The response rate was 56.8%, leading to 1,146 valid responses. After eliminating excessively fast responders and people who used the same answer pattern through sections of the survey, responses from 999 study participants who were suitable for analysis responses were retained for formal analysis.

The resulting proportions were within 0.2% deviation from the quota. About half (51%) of the participants were female. The age of respondents ranged from 18 to over 60 (approximately 20% in each age group), and over 76.9% of them were from New South Wales (NSW), Victoria (VIC) and Queensland (QLD). Most respondents held a college degree or above (77.4%), were employed (68.4%) and had an annual income above AU$36,400 (80.2%; see details in Table 3).

Table 3.

Respondent Profile.

Characteristics	Frequency	Percent	Characteristics	Frequency	Percent
Gender			Bachelor	376	37.6
Female	508	50.9	Master or above	183	18.3
Male	491	49.1	Employment status
Age			Work full time	479	47.9
18–29	219	21.9	Work part-time	205	20.5
30–39	219	21.9	Unemployed	113	11.3
40–49	211	21.1	Retired	148	14.8
50–59	190	19.0	Student	42	4.2
60 over	160	16.0	Disabled	12	1.2
State			Annual income
NSW	318	31.8	$1−$4,199	29	2.9
VIC	251	25.1	$4,200−$8,299	14	1.4
QLD	200	20.0	$8,300−$15,599	12	1.2
WA	110	11.0	$15,600−$25,999	28	2.8
SA	70	7.0	$26,000−$36,399	50	5.0
ACT	20	2.0	$36,400−$51,999	91	9.1
TAS	20	2.0	$52,000−$77,999	170	17.0
NT	10	1.0	$78,000–$103,999	176	17.6
Education			$104,000–$129,999	105	10.5
Below high school	29	2.9	$130,000–$149,999	90	9.0
High school	196	19.6	$150,000 or above	170	17.0
College	215	21.5	Don’t know	64	6.4

Data Analysis

The study employed t-tests and one-way ANOVA with post-hoc Bonferroni tests to detect differences across experimental conditions and identify the most influential heuristic cues for increasing the perceived persuasiveness of carbon offset purchase intentions. Second, following the simple bivariate correlations, the relationships between sociodemographics, online airline ticket purchases, past carbon offsetting purchases, heuristic cues, respondents’ heuristic-systematic mode, and purchase intention were examined by a series of regression analyses (i.e., logic regression, ordinary least squares regression).

Results

Manipulation Check

The study conducted two rounds of pilot tests to ensure the validity of the designed heuristic cues and avoid potential biases in the message design. We provided the concept of each heuristic technique and asked participants to match the designed messages to the type of heuristic cues; 72.16% of participants correctly identified all heuristic cues, suggesting that the messages worked as intended.

We checked the randomization across the experimental groups by examining differences in sociodemographic characteristics, flight frequency and previous voluntary carbon-offsetting purchases across experimental groups. There was no statistically significant difference in age, gender, education, income, frequency of online tickets, and VCO purchase, confirming that the random assignment to the experimental conditions succeeded. Both systematic (Cronbach’s α = .863, M = 4.48) and heuristic (Cronbach’s α = .804, M = 4.07) measurements were above 0.8, ensuring the constructs’ internal consistency and reliability.

Effectiveness of Heuristic Cues

Table 4 presents the mean differences in perceived effectiveness and estimated percentage of carbon offsetting purchases across nine experimental conditions. Participants exposed to the message with the “Primary-Recency” heuristic cue scored the highest in perceived message effectiveness and estimated percentage of carbon offsetting purchase. The anchoring message resulted in the second-highest purchase intention (M_Individual = 55.50, SD = 30.77) and the third-highest estimate of population purchase intention (M_overall = 47.57, SD = 30.05). Except for the experimental group exposed to the “But You Are Free” heuristic cue, seven out of eight experimental groups showed higher scores in individual purchase intention than the control group. However, we only found one statistically significant difference between the control group and the Primacy-recency group (M_Individual = 49.01 vs. 60.85, t = −3.34, df = 224, p < .001; M_overall = 47.82 vs. 55.05, t = −3.34, df = 224, p < .001). Respondents scored higher in perceived message effectiveness based on persuading themselves than others (i.e., estimating the overall percentage of carbon offsetting purchases; See Figure 3).

Table 4.

Mean Results Across Experimental Conditions.

Control/experimental conditions	Individual purchase intention		Overall purchase estimation		N
Control/experimental conditions	Mean	SD	Mean	SD	N
None	49.01	23.47	47.82	23.46	115
Disrupt-Then-Reframe	52.89	29.40	48.18	27.55	107
Legitimization of Paltry Favors	54.65	27.11	45.60	25.68	110
But You Are Free	49.00	31.75	45.69	28.01	111
Foot in the Door	55.10	28.73	46.94	26.95	112
Door in the Face	51.04	33.08	46.59	28.41	113
Anchoring	55.50	30.77	47.57	30.05	110
Imitate the Majority	53.18	27.79	46.21	24.59	110
Primacy-Recency	60.85	29.63	55.05	26.91	111

Note. SD = standard deviation.

Figure 3.

Ranking of mean results.

Factors Affecting Systematic-Heuristic Processing Toward Carbon Offsetting Message

Respondents reported purchasing airline tickets online more than nine times in the past 12 months (M = 9.20, SD = 9.44), whereas only 27.3% purchased voluntary carbon offsets. The study conducted zero-order correlations to assess the association between the variables (Table 5). The variable of systematic processing was significantly related to age, education, past online ticketing, and carbon-offsetting purchase experiences. In contrast, heuristic processing was only relevant to socio-demographics, including gender, age and education.

Table 5.

Zero-order Correlation Analysis.

Variable	Mean	S.D.	1	2	3	4	5	6	7	8	9	10	11
1. Message types	1.88	0.32	–
2. Systematic	4.48	1.07	−0.003	–
3. Heuristic	4.07	1.13	−0.034	0.103^**	–
4. Gender	1.49	0.50	0.047	−0.061	0.085^**	–
5. Age	2.85	1.38	0.027	−0.185^**	−0.173^**	0.254^**	–
6. Education	3.50	1.11	−0.034	0.144^**	0.076^*	0.022	−0.214^**	–
7. Income	8.09	2.59	−0.027	−0.03	−0.057	0.025	0.044	0.136^**	–
8. Online ticket purchase	9.20	9.44	−0.032	−0.122^**	−0.054	0.052	0.135^**	−0.174^**	−0.025	–
9. Offset purchase	1.48	0.91	−0.020	0.228^**	0.014	−0.019	−0.093^**	0.120^**	0.033	−0.064^*	–
10. Individual purchase	53.45	29.28	0.055	0.555^**	−0.084^**	−0.145^**	−0.234^**	0.081^*	−0.081^*	−0.080^*	0.291^**	–
11. Overall purchase	47.74	26.93	−0.001	0.544^**	0.105^**	−0.086^**	−0.327^**	0.119^**	−0.115^**	−0.093^**	0.262^**	0.763^**	–

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

Based on the mean score of heuristic and systematic processing, respondents were first divided into four types, including a low-systematic/low-heuristic group (n = 272, 27.2%), a low-systematic/high-heuristic group (n = 236, 23.6%), a high-systematic/low heuristic group (n = 234, 23.4%), and a high-systematic/high heuristic group (n = 257, 25.7%). We performed multivariate logistic regression analysis to identify the predictive variables on heuristic-systematic processing groups toward carbon offsetting messages. Four binary regression models (i.e., Yes/No as the focused group) included variables including sociodemographics (gender, age, education, income), message types (nine experimental conditions), and past purchase experiences (online airline ticket and carbon offsetting). All four models were statistically significant, χ²_{low-sys/low-heu} = 19.786, p < .01; χ²_{low-sys/high-heu} = 50.092, p < .001; χ²_{high-sys/low-heu} = 50.501, p < .001; χ²_{high-sys/high-heu} = 101.411, p < .001. The models correctly classified the groups, ranging from 72.8% to 76.6%. The Hosmer and Lemeshow test assessed the model’s goodness of fit. P values associated with the tests were above .05, indicating that the four models significantly fit the data (Table 6).

Table 6.

Logistic Regression on Systematic-Heuristic Processing.

Variable	Low-systematic/low-heuristic group			Low-systematic/high-heuristic group			High-systematic/low heuristic group			High-systematic/high heuristic group
Variable	B	SE	OR	B	SE	OR	B	SE	OR	B	SE	OR
Gender	−0.19	0.15	0.83	0.21	0.16	1.23	−0.48^**	0.16	0.62	0.46^**	0.16	1.58
Age	0.11^*	0.06	1.12	0.10	0.59	1.00	0.19^**	0.06	1.21	−0.43^***	0.06	0.65
Education	−0.11	0.67	0.89	−0.11	0.07	0.89	−0.03	0.07	0.97	0.30^***	0.07	1.34
Income	0.00	0.03	1.00	0.05	0.03	1.05	0.04	0.03	1.04	−0.07^*	0.03	0.93
Message types	0.50^*	0.25	1.65	−0.56^*	0.22	0.57	0.06	0.25	1.07	0.04	0.24	1.04
Online airline ticket purchase	0.42	0.03	1.04	0.49	0.34	1.01	−0.18^***	0.05	0.84	0.02	0.03	1.02
Carbon offsetting purchase	−0.05^*	0.03	0.95	−0.18^***	0.04	0.83	0.12^***	0.03	1.12	0.05^*	0.02	1.05
Constant	−1.32^*	0.62	0.27	−0.64	0.60	0.53	−1.23	0.63	0.30	−1.30^*	0.61	0.27
Prediction precision (%)	72.8			76.40			76.60			74.30
χ²	19.786^**			50.092^***			50.501^***			101.411^***
R ² (Cox & Snell)	.02			.05			.05			.14
R ² (Nagelkerke)	.028			.074			.07			.097

Note. B = unstandardized coefficients; SE = standard error; OR = Odds ratio.

***

p < .001. **p < .01. *p < .05.

Results indicate that the odds of applying low systematic processing carbon offsetting messages were significantly impacted by message types (OR_{low-sys/low-heu} = 1.65, p < .05; OR_{low-sys/high-heu} = 0.57, p < .01). Previous purchasing of carbon offsets decreases the probability of using low-systematic (OR_{low-sys/low-heu} = .95, p < .05; OR_{low-sys/high-heu} = .83, p < .001) and increase the possibility of applying high-systemic processing toward the messages (OR_{high-sys/low-heu} = 1.12, p < .001; OR_{high-sys/low-heu} = 1.053, p < .001). However, past online airline ticket purchasers are 0.84 times less likely than others to fall into the high-systematic /low-heuristic processing group. High-systematic groups were significantly related to sociodemographic factors, including gender, age, education, and income. Female respondents were more likely to be included in the high-systematic/low-heuristic group (OR = 0.62, p < .01) yet less likely to be the high-systematic/high-heuristic individuals (OR = 1.58, p < .01). Respondents in high-systematic/high-heuristic group were more likely to be young (OR = 0.65, p < .001) and high-income respondents (OR = 1.34, p < .001). In contrast, older respondents increased the possibility of being categorized as the low heuristic group (OR_{low-sys/low-heu} = 1.12, p < .05; OR_{high-sys/low-heu} = 1.21, p < .01).

Impacts of Systematic-Heuristic Processing on Carbon Offsetting Purchase Intention

Correlation analysis revealed that respondents’ individual carbon offsetting purchase intention was significantly related to message types (i.e., different persuasion techniques), systematic-heuristic processing, sociodemographics, past online tickets, and past carbon offset purchase experience. There was a strong positive relationship between systematic processing and individual offset purchase intentions (r = .555, p < .01). However, heuristic processing was negatively related to individual carbon offset purchase intention (r = −.084, p < .01). Further, we conducted one-way ANOVA followed by a Duncan posthoc analysis to examine the differences in individual purchase intention and overall purchase estimation toward the carbon offset messages among the four groups. Results indicate that systematic-heuristic types significantly affect purchase intention (_Individual = 107.65, p < .001; F_overall = 94.70, p < .001). Each of the two groups found statistically significant differences. The high-systematic/low-heuristic group scored the highest on individual carbon offset purchase intention (M = 69.09), while the high-systematic/high-heuristic group demonstrated the highest estimation of overall carbon offset purchase (M = 64.08). Low-systematic groups scored much lower than the systematic group in carbon offset purchase intention. Low-systematic/high-heuristic group respondents showed the lowest intention to purchase carbon offsets, M_individual = 32.82, M_overall = 32.45 (see Figure 4).

Figure 4.

Comparison across four systematic-heuristic groups.

The study performed hierarchical regression to examine further the impact of systematic-heuristic processing on carbon offsetting purchase intention. The analysis used sociodemographic variables as control constructs and added other independent variables to identify their impact on air travelers’ intention to purchase carbon offsets. The first model with the control variables explained 7% of the variance in individual carbon offsetting purchase intention and 12.1% in overall carbon offsetting purchase estimation. The explanation of the dependent variables increased most by adding systematic-heuristic processing factor (adjusted R²_individual = .361, p < .001, adjusted R²_overall = .357, p < .001), followed by past purchase experience (adjusted R²_individual = .144, p < .001; adjusted R²_overall = .176, p < .001) and message types (adjusted R²_individual = .074, p < .001; adjusted R²_overall = .123, p < .001) factors. The factors explained 39.1% of the variance in respondents’ purchase intentions and 37.6% in overall purchase estimation.

Standardized β indicated specific variables in predicting air travelers’ carbon offset purchase intention. Systematic processing (β_individual = .540, p < .001; β_overall = .497, p < .001) had the largest impact on individual purchase intention and overall purchase estimation. Past carbon offsetting purchase was the second largest predictor in individual purchase intention (β = .272, p < .001) and the third largest predictor in overall purchase estimation (β = .232, p < .001). Heuristic processing negatively impacted individual purchase intention (β = −.163, p < .001), whereas overall purchase estimation had no significant influence. Among the sociodemographic variables, age (β = −.196, p < .001), gender (β = −.094, p < .001) and income (β = −.078, p < .01) played important roles in predicting individual purchase intention. People who are young females with lower income would be more willing to purchase carbon offsetting after seeing the message. However, education (β = .072, p < .05) rather than gender predicted respondents’ overall purchase estimation (Table 7).

Table 7.

Hierarchical Regression Results.

	Individual purchase intention		Overall purchase estimation
Variable	β	SE B	β	SE B
Sociodemographics (F1)
Gender	−.094^***	1.858	−.007	1.661
Age	−.196^***	0.691	−.305^***	0.617
education	.051	0.835	.072^*	0.747
income	−.077^**	0.351	−.110^***	0.314
Message types (F2)	.064^*	2.806	.007	2.514
Systematic-heuristic process (F3)
Systematic	.540^***	0.712	.497^***	0.657
Heuristic	−.163^***	0.678	.008	0.625
Past purchase experience (F4)
Online airline ticket purchase	−.034	0.093	−.036	0.084
carbon offsetting purchase	.272^***	0.951	.232^***	0.858
Constant		6.044		5.627
Model 1: F1 R²	7.0^***		12.1^***
Model 2: F1 + F2 R² change (%)	7.4^***		12.3^***
Model 3: F1 + F3 R² change (%)	36.1^***		35.7^***
Model 4: F1 + F4 R² change (%)	14.4^***		17.6^***
F-value	70.490^***		66.138^***
Adjusted R² (%)	39.1^***		37.6^***

Note.β = standardized coefficients. SE B = standard error.

***

p < .001, **p < .01, *p < .05.

Study 2: The Experimental and Qualitative Research

The study used the desktop-mounted eye-tracker Tobii TX-300 in a laboratory setting, which recorded eye movements at 300 Hz using infrared corneal reflection with a 0.5° precision. Eye movements include fixations and saccades (Duchowski, 2007; Rayner, 1998). Fixations last for about 200 to 300 ms. Saccades are short (20–40 ms) rapid eye movements between fixations during which information processing is suppressed (Rayner, 1998). When reading English, eye fixations last about 200 to 250 ms and range from just under 100 to over 500 ms (Rayner, 1998).

Design

A within-subject design was conducted in a laboratory. The sample included 40 respondents, including staff and students at a large Australian university. This sample size is typical of eye-tracking. The only requirement for participation was that participants had booked a flight online in the last 3 to 5 years. To address potential limitations related to the small sample size and controlled setting, we incorporated real-time physiological measurements and post-experiment interviews to capture participants’ immediate reactions and subjective understanding of the messages. This approach enhances the robustness of the findings by integrating quantitative and qualitative insights.

Eight alternative messages were tested to measure attention due to exposure to the messages. They were designed based on insights from prior work. One message represents the current message airline passengers see when they book a flight online with Qantas, the Australian national carrier. Messages were rotated among participants to avoid an ordering effect. All messages except the Heuristics (Message 8) are almost at the same difficulty level (Flesch, 1948; Table 8).

Table 8.

Experiment Stimuli.

Message	Stimuli	Messages inform	Flesch score	Number of word
1	Qantas	Basic facts	57: (fairly difficult)	114
2	Transparency	Statistic	16.8 (very difficult)	41
3	Transparency	Non-statistic	56.4: (fairly difficult)	13
4	Knowledge	Trustworthy	16.8: (very difficult)	41
5	Knowledge	Untrustworthy	52.9: (fairly difficult)	20
6	Efficacy	Statistic	41.7: (difficult)	42
7	Efficacy	Non-statistic	52.4: (fairly difficult)	21
8	Heuristics cues	Primacy-recency	69.3: (Plain English)	21

Implementation

The study was conducted at a university eye-tracking laboratory from November to December 2020. We informed the participants that a camera would record their eyes while reading the messages. Next, the participants sat in front of the eye-tracker. The distance between participants and the eye-tracker was approximately 60 cm. After a nine-point calibration process on the eye-tracker screen, participants started looking at the messages while their eye movements were recorded. To maintain ecological validity, participants were not restricted by a time limit and were instructed to read the messages at their own pace. However, the total time spent on each message was recorded to assess variations in engagement across different message conditions. The university’s Human Ethics Committee approved the experiment protocol.

After answering all survey questions, study participants were requested to comment on which message(s) and what part of the message(s) they pay attention to most and least, and their understanding of the messages. Finally, we asked participants to suggest improving the carbon offsetting message. Such verbal feedback is valuable because eye movements accurately show what respondents look at, but they cannot explain why respondents look at the specific text (Duchowski, 2007). Respondent comments were recorded and served as an additional source of insight. The task took 15 to 20 min; participants were debriefed afterward and received a $30 gift voucher.

Data Analysis

We processed the eye-tracking data with the iMotions software and defined the areas of interest around the entire message on the screens. Gaze data with a minimum duration of 100 ms is considered a fixation. As indicators of processing depth, we used the following two measures (Russo, 2019): Total time spent (in seconds-s), total duration of all respondents’ fixations in the area of interest, and Average fixation duration (in milliseconds-ms): average of all respondent’s fixation times inside the area of interest. The duration of fixations indicates the extent to which respondents cognitively process information (Rayner, 1998; Russo, 2019). Non-parametric Friedman’s tests determined whether fixation times and average fixation durations differed significantly across messages.

Results

Sample Profile

The participants were all university students and staff, primarily female (75%) and between 18 and 39 years old (87%). Participants reported they took between one and 10 flights in a typical pre-COVID year. Most participants (78%) were aware of carbon offsetting, and more than half (55%) knew what carbon offsets are.

Relative Attention to the Messages

All eight messages were analyzed for fixation time. The result shows significant differences among them (χ²(7) = 182.80, p = .00). Specifically, “Qantas” (Message 1), “Efficacy” (Messages 6) and “Transparency” (Message 2) received the highest fixation times (Median = 13.84, 9.62, and 8.88 s, respectively). The reason that “Qantas” (Message 1) received the higher fixation time is probably because the “Qantas” (Message 1) was three to fourth times longer (114 words compared to 20 words (short messages) and 40 words (long messages)) than other messages. “Transparency” (Message 3), “Efficacy” (Message 7), and “Heuristic” (Message 8) received the lowest fixation time (Median = 3.36, 4.00, and 4.21 s, respectively).

Because the messages had different lengths, we adjusted for the number of words to compare attention to the message. Figure 1 shows box plots of adjusted fixation time for each message—the y-axis plots fixation times per word in milliseconds. The x-axis indicates messages. The orange boxes indicate short messages, and the blue boxes show long messages. Values more than 1.5 interquartile range from the end of a box but less than three interquartile range from the end of a box are labeled as outliers (o), and values more than three interquartile range from the end of a box are labeled as extremes, denoted with an asterisk (*).

Adjusting for the number of words shows a significant difference in fixation time among messages. “Qantas” (Message 1), “Efficacy” (Message 7) and “Heuristic” (Message 8) received the lowest fixation time per word (Median = 121.46, 190.50, and 200.81 ms, respectively). “Knowledge (Message 5),”“Transparency” (Message 3), “Efficacy” (Message 6), and “Transparency” (Message 2) received the highest fixation time per word (Median = 288.03, 250.92, 229.13, and 216.65 ms, respectively; Figure 5).

Figure 5.

Fixation time per word for each message.

A step-down follow-up analysis of Friedman’s test shows that only the “Qantas” message received a significantly lower fixation time per word than the other messages (“Message 2–7”). The result also shows no significant difference in average fixation duration among messages, indicating that all the messages were processed similarly.

Comparing fixation time and fixation time per word analysis suggests that participants pay more attention to the messages “Transparency” (Message 2) and “Efficacy” (Message 6). In contrast, the “Heuristics” (Message 8) and “Efficacy” (Message 7) received the lowest attention.

The qualitative comments that respondents provided after seeing all the messages support the proposition that “Transparency” (Message 2) and “Efficacy” (Message 6) were the most attractive messages. Most participants (70%) stated that “Transparency” (Message 2) was the most attractive message. Quantitative information (like percentage) about carbon offset programs and specific projects (e.g., Amazon, Tasmania) were the most attractive information within the messages: “I think the messages that were more descriptive with more detail that tells you what exactly the positive impact is or how the money has been used.” (female). “I perfect numbers and the specific projects I remember, a certain percentage goes to the rainforest community, certain go to Cambodia, and 0% to the airline” (female).

Figures 6 and 7 show the fixation time heat maps for “Transparency” (Message 2) and “Efficacy” (Message 6). The heat map shows the relative intensity of fixation time captured by the eye-tracker by assigning each fixation time a color representation. Those that are highest in value—relative to the other present numbers—will be given a “hot” color, while those that are lower in value will be given a “cold” color. It is clear from heatmaps that specific quantitative information (particularly 0% to the airline) and specific projects like “Tasmania wildlife” and “rainforest community” attracted the most attention.

Figure 6.

Heatmap of “Transparency” (Message 2).

Figure 7.

Heatmap of “Efficacy” (Message 6).

The qualitative comments of participants further support that the “Efficacy” (Message 7) and “Heuristics” (Message 8) received the lowest attention. Participants commented they did not like the short messages (Message 3, 5, 7), specifically the “Efficacy” (Message 7), because the message(s) was generic and vague: “They all go to environmental project does not explain much anyone can say that” (female).

Participants wanted to avoid the wording of the “Heuristics” (Message 8) message. They commented that they preferred more positive outcomes of their contribution rather than adverse outcomes: “I think it (‘Heuristics’) was a bit too aggressive; it did not have a message” (male). Figure 8 shows the heat map of the “Heuristics” (Message 8). The heatmap again confirms that the words “threat” and “real” were the most attractive information.

Figure 8.

Heatmap of the “Heuristics” (Message 8).

Discussion

Through self-reported and psychophysiological research approaches, this paper studies carbon offsetting message effectiveness through a lens of systematic and heuristic information processing. Despite previous research highlighting that people tend to employ mental shortcuts in processing online purchase information (Hlee et al., 2018; Nazlan et al., 2018), the study reveals that people prefer systematic information rather than heuristic cues in the context of carbon offsetting. While heuristic cues, particularly primacy-recency and anchoring, were rated as the most persuasive in the survey, eye-tracking results suggest that systematic messages emphasizing transparency, and efficacy garnered significantly higher attention and engagement.

The divergence between self-reported and eye-tracking results highlights a gap between perceived persuasiveness and actual attention. Although participants found heuristic cues in the survey, eye-tracking data revealed that systematic messages captured significantly more visual engagement. Heuristic cues may create immediate emotional appeal, leading to higher self-reported persuasiveness rather than raised attention. Conversely, systematic messages may require more significant cognitive effort, resulting in higher fixation times and deeper engagement with the content. The study indicates that heuristic cues improve self-reported willingness to purchase carbon offsets, while statistically framed VCO messages with transparency and efficacy information represent the optimal communication design for capturing people’s attention. The results underscore the importance of using perceptual and behavioral data to assess message effectiveness in VCO communication.

Theoretical Implications

This investigation makes several contributions to knowledge. First, it fills a research gap by examining the effectiveness of using heuristic cues in carbon offsetting message design. Although previous research has confirmed the usefulness of heuristic cues in online consumer reviews (e.g., Hu & Yang, 2020; Nazlan et al., 2018), the role of heuristic cues has been largely ignored in voluntary aviation carbon offset purchasing. For the first time, this study investigates the role of heuristic cues in carbon offsetting communication, concluding that the availability heuristic (i.e., primacy-recency technique) is most effective in increasing people’s carbon offset purchase intention. Consistent with research on retail and hotel purchases (e.g., Tanford et al., 2019), results show that anchoring techniques can impact the price perception of flight carbon offsetting, which triggers voluntary purchase intention. However, not all social heuristic cues are effective in the VCO context. Despite the common belief that pro-environmental behavior is associated with social and moral norms (Choi et al., 2016; Tyers, 2018), social influence techniques did not increase purchase intention. Notably, respondents who were presented with messages with “But You Are Free” cues expressed a lower level of prosocial behavioral intention, which stands in contrast with findings from previous studies (e.g., Carpenter, 2013; Guéguen et al., 2010; Meineri et al., 2016). This may be due to the difference between face-to-face and internet communication. Given that some social heuristic techniques rely on respondents’ positive self-presentation in front of the requester (Anker et al., 2010), these approaches are more effective in online purchase situations. Among the social heuristic cues, “Foot in the Door” and “Legitimization of Paltry Favors” emerge as most promising techniques in the context of VCO communication, supporting conclusions drawn in prior studies (e.g., Boos et al., 2024; Chi et al., 2021; Nai et al., 2022), which reveal that a gain-framed message is more effective in changing carbon-offsetting behavioral intentions than a loss-framed message.

Second, this study contributes new knowledge by exploring the factors of systematic-heuristic thinking that drive online flight carbon offset purchases. Results indicate that age and education determine heuristic and systematic processing modes, while gender only affects heuristic processing. Women emerge as more rational carbon offset purchasers and are more likely to use high-systematic/low-heuristic processing when exposed to carbon-offsetting communication messages. Older men are more likely to be heuristic-induced purchasers who use high-systematic/low-heuristic and low-systematic/low-heuristic processing when exposed to carbon offset communication messages. The role of gender in heuristic thinking aligns with findings from previous research (e.g., Richard et al., 2010; Ryu & Kim, 2015). Women are comprehensive processors integrating all provided information (Meyers-Levy & Maheswaran, 1991). Women engage more in the detailed elaboration of carbon-offsetting messages on online flight booking websites than men. In line with previous studies on age-associated effects on dual-process (e.g., Hess et al., 2001; Yoon et al., 2009), our study shows that older people tend to undertake heuristic processing toward carbon offsetting messages. Given the low awareness and knowledge of carbon offsetting (Ritchie et al., 2019), older people may conserve mental energy by thinking about carbon offsetting issues in a heuristic way. Conversely, young, high-income consumers who can comprehend online information prefer to apply high systematic-heuristic processing styles.

The study also extends the literature on profiling carbon-offsetting purchasers by examining the factors of purchase experience and sociodemographics on carbon-offsetting message persuasion (McLennan et al., 2014). Using messages, young women can be more easily nudged to purchase carbon offsetting than men. The persuasion effects of carbon offsetting messages are higher among past carbon offsetting purchasers, further supporting the importance of carbon offsetting knowledge and awareness (Mair, 2011). Past online purchase experiences, including flight ticketing and carbon offset purchases, are positively associated with more systematic processing. Previous research confirms that high involvement can increase the motivation to process issue-related information, which leads to systematic processing (Chaiken, 1980). Thus, previous online purchase experience can promote air travelers’ knowledge and engagement in carbon offsetting issues, thus triggering their systematic thinking about carbon offsetting messages. Further, the study provides insights into message communication by examining the effects of heuristic cues on dual-process mode. However, the research did not detect significant impacts of heuristic cues on systematic heuristic processing. Respondents who were presented with heuristic-induced messages preferred low-heuristic thinking toward the information. Studies argued that people exert additional effort instead of reducing cognitive energy to comprehend heuristic cues (Fiske & Taylor, 1991; Nazlan et al., 2018). Messages with heuristic cues can increase people’s cognitive processing capacity, enhancing their systematic processing.

Third, in response to the sparse but growing research of carbon offsetting communication (Babakhani et al., 2017; Zhang et al., 2019b), this study contributes to a broader understanding of how people may apply heuristic-systematic processing on online carbon offset purchases. People who employ high-systematic/low-heuristic processing on carbon offsetting messages are more willing to purchase aviation carbon offsets. People in the high-systematic/high-heuristic group assume that over half of customers purchase carbon offset products. Systematic processing significantly increases people’s individual purchase intention and overall purchase estimation, which aligns with previous green purchase research (e.g., Yang et al., 2020). People who use systemic processing apply deductive logic to evaluate information and behavioral outcomes. Therefore, they may attach more importance to environmental issues and increase the adoption of pro-environmental behaviors. Heuristic processing decreases individuals’ carbon-offsetting purchase intention. Despite studies that found that people may rely on mental shortcuts in online consumption (e.g., Ruiz-Mafe et al., 2018), we argue that carbon offset purchase is driven by thoughtful consideration rather than heuristic thinking.

Finally, the study integrates a psychophysiological approach to measure attention and engagement with environmental communication objectively, adding new knowledge to tourism and pro-environmental research. The findings highlight that transparency and efficacy are the most effective elements in VCO communication design, mainly when supported by statistical information. Consistent with previous research (Babakhani et al., 2017), the findings confirm that understandable, detailed information with numbers can better attract attention and engage individuals in decision-making. However, in contrast with previous assumptions that heuristic cues (e.g., anchoring and framing) are effective in influencing pro-environmental behavior (Andersson et al., 2021; Bimonte et al., 2020), the research reveals that travelers favor credible and transparent information over heuristic shortcuts in the carbon offsetting context. Thus, the finding challenges established assumptions of heuristic cues in prosocial messages, adding novel insights to dual-process theories by demonstrating the advantages of systematic processing in complex and value-driven decisions.

Practical Implications

The research offers practical implications that can be applied to carbon offsetting design, aiming to improve people’s awareness and willingness to pay for carbon offsets. First, the study provides insights into how online customers apply heuristic-systematic thinking to process carbon-offsetting messages. Carbon offsetting practitioners may consider how to induce the public to exert systematic thinking toward carbon offsetting messages. Thus, to improve online air travelers’ systematic processing and purchase intention, airlines could use heuristic cues in communication, including (1) recalling recent events, (2) using a high price anchor, and (3) highlighting the small cost and significant contribution. To seamlessly integrate these cues into the booking interface, airlines could employ pop-up notifications, hover-over tooltips, or brief in-line messages to minimize disruption to the user experience. Travel agencies may adopt heuristic cues, specifically the primacy-recency approach, to improve persuasion effectiveness. For instance, information about recent natural disasters can help raise air travelers’ perception of climate change, which is more effective in VCO promotion.

Second, the study confirms that individuals prefer rational thinking over heuristic cues when making VCO purchase decisions. Thus, airlines could provide numerical data rather than extensive textual information on VCO on the airline’s online booking website. For instance, instead of generally explaining how carbon emissions were calculated, the airlines’ websites can employ more concrete numbers to explain the efficacy of the carbon offsetting projects (e.g., the projects can contribute to 10 million tree planting). Moreover, travel agencies need to improve transparency by providing more straightforward information on how carbon offsets are spent. For example, a website link can be attached for air travelers to further explore more information on the VCO projects, offering actual photos and outcomes of sponsored projects.

Third, the identified four clusters of air travelers can help airlines and other companies better understand VCO purchasers’ heterogeneity. Notably, carbon offsetting promoters need to consider message receivers’ age, gender, education, travel frequency, and past carbon offsetting experiences in advertisement distribution. For instance, detailed VCO messages can be specially designed for young business travelers, who can engage more cognitive efforts in assessing carbon-offsetting messages and considering purchasing the products. For the VCO skeptics, it is necessary to provide information by emphasizing common knowledge, social norms, and the cost/benefits of carbon offsetting. For the elderly male frequent flyers, heuristic cues are ineffective in VCO communication. Thus, travel agencies may offer understandable and practical offers to improve the group’s interest in VCO purchases. For university students, online booking websites can provide more package options to increase the young generation’s engagement and contribution to carbon offsetting.

Limitations and Future Research Directions

The current research has limitations that can be overcome in future research. Firstly, data were collected via an online survey and a lab experiment with air travelers residing in Australia, limiting the generalizability of findings. While Australia is a developed Western country with relatively high environmental awareness and policy support, future research could explore the effectiveness of heuristic cues in developing Eastern countries where carbon offsetting promotion and participation remain low. Secondly, since this study is limited to a one-time measurement approach to assess the effectiveness of heuristic and systematic cues, future studies could adopt a longitudinal design to track how message processing styles and engagement evolve with repeated exposure. Thirdly, this study investigated self-reported carbon offset purchase intention and overall purchase estimation, which could be impacted by social desirability bias (Auger & Devinney, 2007). To address this, future studies could incorporate validated social desirability scales, such as the Marlowe-Crowne Social Desirability Scale (Reynolds, 1982) or the Balanced Inventory of Desirable Responding (Hart et al., 2015), to statistically control for such biases. Moreover, future research could collect purchase records by launching field experiments using airline booking websites and booking data.

Footnotes

Author Contributions

Danni Zheng: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration.

Brent W. Ritchie: Funding acquisition, Project administration.

Sara Dolnicar: Funding acquisition, Project administration.

Nazila Babakhani: Investigation.

Submission Declaration and Verification

The authors of this research confirm that the work described has not been published previously, that is not under consideration for publication elsewhere, that its submission and publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the Australian Research Council (ARC) [project LP150101001].

ORCID iDs

Danni Zheng

Brent W. Ritchie

Sara Dolnicar

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