Green Algorithms: The Impact of Artificial Intelligence on Environmental Sustainability †
1
Department of Foreign Languages, Sichuan Tourism University, Chengdu 610100, China
2
Department of Business Administration, Jinnah University for Women, Karachi 74600, Pakistan
3
Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S0A2, Canada
*
Author to whom correspondence should be addressed.
†
Presented at the 1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024), Regina, Canada, 27–29 June 2024.
Eng. Proc. 2024, 76(1), 40; https://doi.org/10.3390/engproc2024076040
Published: 24 October 2024
(This article belongs to the Proceedings of 1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024))
Abstract
:This study examines how artificial intelligence (AI) can support environmental sustainability. Many businesses use AI technologies to enhance environmental sustainability and motivate customers to adopt eco-friendly practices. We investigate how these factors affect the adoption of sustainable products through an empirical study involving a variety of types of consumers. Our study’s findings have major implications for the academic and business communities. Marketing professionals and decision-makers can learn substantial information about how to use AI technologies to engage customers and advance environmental well-being. Organizations can create targeted strategies that promote sustainable behaviors and have a positive impact on the environment by understanding the factors that influence product adoption.
1. Introduction
Some of the main significant improvements over the five years prior revolved around how effectively AIs function in vast information environments on certain sorts of employment possibilities. In the context of future potential, AIs can strengthen and support humans [1]. AI includes autonomous actions to tackle environmental problems. An environmentally sustainable product poses fewer problems due to its materials, manufacturing, distribution, disposal/recycling, and functionality [2].
To address environmental issues, companies have created AI-powered products like robots that engage with the environment, enabling them to complete tasks [1].
There is a lack of knowledge on the use of autonomous environmental benefits to attract new customers. AI-enabled environmental sustainability of products may pose resistance to companies [3]. This study tries to close the literature gap and pinpoint business strategies to profit from the creation of environmentally sustainable products. Additionally, it differentiates the effect of static and autonomous environmental advantages on consumer groups, consumer locations, and product categories. The following research questions are answered by the study in terms of environmental sustainability:
- How does the inclusion of AI in products affect consumer purchase intentions and appeal to new consumer categories?
- What are the main determinants of the efficacy of AI-based environmental sustainability in product marketing to various consumer types and product categories?
- What are the factors that prevent consumers from embracing artificial intelligence (AI)-based environmental sustainability when buying products, and how can businesses overcome this obstacle to successfully market environmentally sustainable products in this market?
2. Literature Review
2.1. Theoretical Framework
The theoretical framework of the paper is in Figure 1 below.
2.2. Product Adoption
Understanding new product adoption behavior is critical for both firms and researchers striving to explain and influence consumers’ decisions. Influencing consumers through traditional advertising seems to become less effective [4]. One of the key objectives of viral marketing is to identify a small set of users in a social network who, when convinced to adopt a product, will influence others in the network, leading to a large number of adoptions in an expected sense. New products force consumers to change their behavior, and that has a psychological cost. Many products fail because people irrationally over-value the benefits of the goods they own over those they do not possess. Executives, meanwhile, overvalue their own innovations. This leads to a serious clash. Marketing practitioners and researchers seem to agree that social network data help to facilitate targeted marketing and influence consumers’ new product adoption behavior. The presumed marketing potential of structural data from social network platforms such as Facebook and LinkedIn is visible in these firms’ high market valuations. In addition to this comparison, this study investigates which consumers firms should target. Some research challenges long-accepted truths about the importance of advisers or opinion leaders in product adoption processes [5], suggesting that social contagion is better explained by status considerations than by social learning under uncertainty.
2.3. Hypothesis Development
Hypothesis H1.
Autonomous environment AI benefits have a positive impact on Customer-initiated engagement.
Hypothesis H2.
Customer-initiated engagement has a positive impact on product adoption.
Hypothesis H3.
Autonomous environment AI benefits have a positive impact on perceived environmental well-being.
Hypothesis H4.
Perceived environmental well-being has a positive impact on product adoption.
Hypothesis H5.
Perceived environmental well-being mediates the relationship between autonomous environment AI benefits and product adoption.
Hypothesis H6.
The need for recognition moderates the effects of customer-initiated engagement on product adoption.
3. Methodology
A questionnaire-based survey of consumer attitudes toward AI-enabled products helped measure variables and test the hypothesis about the cause of variation in purchase intent. The data were collected from students and consumers involved in environmental sustainability.
In order to test autonomous environmental behavior, a 4-question scale from [6] was used. Customer engagement was measured using a 7-question scale from [7], environmental well-being was measured using a 4-question scale from [8], product adoption was measured using a 3-question scale from [9], and need for cognition was measured using a 4-question scale from [10]. The present study used Smart PLS (3), a statistical tool to examine the data through partial least square equation modeling (PLS-SEM). The data/sample properties, as well as the process of moderation and mediation evaluation, led to the decision to choose this analysis technique. Similarly, this technique has acquired relevance in research on product adoption, environmental sustainability, marketing, and other domains.
4. Results
4.1. Common Bias Method
This study tested the common method bias of collected data; it may occur due to consistent interest and social media. Following [11], Harman’s 1-factor test was conducted with the multiple constructs in the current research model, including autonomous environmental behavior, need for cognition, product adoption, environmental well-being, and customer engagement, so the sample used in this study has no significant concern with regard to common method bias [12].
4.2. Variance Inflation Factor (VIF)
VIF is conventional and perhaps the important one for analyzing common method bias. The VIF values of AEB1, AEB2, AEB3, and AEB4 are 1.040, 1.076, 1.169 and 1.093. The VIF values of CE1, CE2, CE3, CE4, CE5, CE6, and CE7 are 1.162, 1.240, 1.225, 1.304, 1.279, 1.189 and 1.166. The VIF values of EWB1, EWB2, EWB3, and EWB 4 are 1.172, 1.323, 1.318 AND 1.130. The VIF values of NOC1, NOC2, NOC3, and NOC4 are 1.297, 1.216, 1.078, and 1.077. The VIF values of PA1, PA2, PA3 are 1.197, 1.259 and 1.000. Table 1 summarizes the values of VIF.
4.3. Internal Consistency
The current study examined Cronbach’s values to evaluate the constructs’ consistency within themselves. The results are presented in Table 2. The Cronbach’s alpha values were above the threshold of 0.70 set by [13]: AEB (α = 0.450), CE(α = 0.645), EWB (α = 0.638), NOC (α = 0.512), and PA (α = 0.528).
4.4. Convergent Validity
Table 2 summarizes the result of composite reliability (CR) and average variance extracted (AVE). CR values were above the threshold of 0.7 [14]: AEB (CR = 0.699), CE (CR = 0.764), EWB (CR =0.786), NOC (CR = 0.722), and PA(CR = 0.760). The AVE values were also above the threshold of 0.50 [15]: AEB (AVE = 0.369), CE (AVE = 0.355), EWB (AVE = 0.480), NOC (AVE = 0.369) and PA (AVE = 0.516). Factor loadings were significant, and t-values were above the threshold value of 0.50 [14]. The values of CR > 0.7 [14] and AVE > 0.5 [15] were above the threshold values and fulfilled the standard requirements for validity [16].
4.5. Predictability of the Model
This study assessed the reliability of the model by using R square values, as shown in Table 3. The adjusted r-square values exceed 0.10, indicating that this model has appropriate prediction potential.
The Structural diagram is shown in Figure 2.
4.6. Hypothesis Results
Table 4 shows the hypotheses testing, which indicates that two hypotheses out of six are found to be supportive. Autonomous environmental benefits (H1) have a significant determinant influence on customer-initiated engagement with t-statistics 4.685 (p = 0.000). Customer engagement (H2) has a significant impact on product adoption with t-statistics 2.596 (p = 0.009). Similarly, the use of autonomous environmental benefits (H3) significantly influences environmental well-being, with t-statistics of 1.716 (p = 0.086). Environmental well-being also significantly impacts product adoption, with a t-value of 3.888 (p = 0.000). Environmental well-being does not have a significant determinant influence on product adoption, with t-statistics of 3.888 (p = 0.000). Product adoption, as a moderator, does not have a significant but has a positive impact on the need for cognition and customer engagement. Environmental well-being mediates the relationship between autonomous environmental benefits and product adoption.
5. Discussion
Hypothesis (H1) states that AI benefits positively influence the need for customer-initiated engagement. The current study shows that AI benefits do not use a considerable positive influence on customer-initiated engagement with the effect size (β = 0.024). These results validate the previous studies that found that a consumer’s requirement for cognition reduces the impact of autonomous environmental advantages on purchase intent for two distinct reasons. First, autonomous environmental advantages emerge through the initial use of an AI-enhanced product instead of the pre-purchase period, making them easier to notice and grasp. Second, the primary attraction is that these acts are autonomous and free the customer from making judgments. Therefore, autonomous environmental benefits could be appealing more to consumers with little need for cognition [17]. Environmental benefits represent an impact on society through the actions of AI-enhanced goods and, as a result, also serve as a signal of trustworthiness and socially desirable values [18].
Hypothesis (H2) states that customer engagement positively influences product adoption. The current study shows that the need for cognition positively influences product adoption with the effect size (β = 0.017). The current result validates previous studies. When consumers interpret the environmental sustainability of a product as a more valuable signal to their situation, they are more likely to purchase the product [17]. Customers with a high need for cognition positively influence product adoption through thorough evaluation, perceived quality, and increased engagement.
Hypothesis (H3) states that environmental well-being is positively influenced by autonomous environmental benefits with the effect size (β = 0.016), stating that perceived autonomous environmental advantages have a smaller influence on product purchase intent in consumer localities with higher environmental well-being. In the opinion of [19], a product’s environmental advantages could suggest its trustworthiness. The significance of this signal relies on the consumer’s level of protection for the person for whom it is intended. Because humans intuitively protect children, signs of trustworthiness are of greater significance to adult buyers when buying children’s items. Therefore, the impact of static environmental advantages as an indicator of trustworthiness on purchase intent is larger when adult consumers buy children’s items (e.g., toys) than when they buy adult products (e.g., vehicles).
Hypothesis (H4) states that environmental well-being positively influences product adoption with the effect size (β = 0.012). The current result validates previous studies that argue that the effect of autonomous environmental advantages on purchase intent is less effective while adult customers purchase goods targeted at kids [17].
Hypothesis (H5) states that perceived environmental well-being mediates the relationship between autonomous environmental AI benefits and product adoption. The current study shows that perceived environmental well-being has a considerably positive influence on autonomous environmental AI benefits and product adoption with the effect size (β = 0.008). The new conclusion demonstrates prior studies, which indicate that signal improves the consumer’s sense of quality perception [18], pinpointing with the brand [20]. As a result, these procedures increase the consumer’s intent to buy the goods [21].
Hypothesis (H6) states that customer-initiated engagement does not moderate the relationship between the need for cognition and product adoption. The current study shows that customer-initiated engagement uses a considerably positive influence on the need for cognition and product adoption with the effect size (β = −0.005). The current result validates the previous studies that suggest the differences between genders. Based on scientific research, women tend to be more unwilling to take risks than males, making them more sensitive to indications of trustworthiness [22]. According to [23], this also relates to their choices regarding purchases. As a result, the literature demonstrates that environmental sustainability has more of an effect on consumer behavior in women compared to males.
6. Conclusions and Limitations
This study offers insightful information on the dynamics of customer behavior regarding items improved by artificial intelligence. The results underscore the significance of customer involvement in the adoption phase of products and the influence of environmental perceptions on consumer choices. The study highlights how consumers’ reactions to AI capabilities in goods can be nuanced, even though no substantial correlation was found between the advantages of AI and customer-initiated interaction. Additionally, the study highlights the complex relationship between autonomous environmental advantages and environmental well-being as well as product uptake, especially when taking the consumer’s location and intended product user into account. This implies that based on the particular setting and target market, the efficacy of AI-enhanced goods in increasing environmental well-being and product uptake may differ. It is important to take into account the many limitations of this study when evaluating the results. Firstly, the study’s impact sizes were found to be rather tiny, indicating that the intricate interactions between the factors may be more nuanced than first thought. Secondly, the research being conducted is cross-sectional, which restricts the capacity to make conclusions about causality. Furthermore, the study’s sample was restricted to a certain demographic, which might have constrained how broadly the results could be applied. In order to investigate these correlations across many people and circumstances, more studies are required. Finally, as the study only examined a limited number of variables, additional factors could potentially have an impact on how consumers behave when considering AI-enhanced items.
Author Contributions
Conceptualization, S.I.Z.; methodology, S.I.Z. and S.T.J.; software, S.A.K.; validation, S.I.Z.; formal analysis, S.A.K.; investigation, S.I.Z.; resources, S.A.K.; data curation, S.I.Z.; writing—original draft preparation, S.T.J. and S.I.Z.; writing—review and editing S.I.Z. and S.T.J.; visualization, S.A.K.; supervision, S.I.Z.; project administration, S.A.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Informed consent was obtained from all subjects.
Data Availability Statement
Dataset available on request from the authors.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Theoretical Framework.
Figure 2.
Structural diagram.
Table 1.
VIF.
| Constructs | VIF |
|---|---|
| Autonomous environmental benefits (AEB1) | 1.040 |
| Autonomous environmental benefits (AEB2) | 1.076 |
| Autonomous environmental benefits (AEB3) | 1.169 |
| Autonomous environmental benefits (AEB4) | 1.093 |
| Customer engagement (CE1) | 1.162 |
| Customer engagement (CE2) | 1.240 |
| Customer engagement (CE3) | 1.225 |
| Customer engagement (CE4) | 1.304 |
| Customer engagement (CE5) | 1.279 |
| Customer engagement (CE6) | 1.189 |
| Customer engagement (CE7) | 1.166 |
| Environmental well-being (EWB1) | 1.172 |
| Environmental well-being (EWB2) | 1.323 |
| Environmental well-being (EWB3) | 1.318 |
| Environmental well-being (EWB4) | 1.130 |
| Need of cognitions (NOC1) | 1.297 |
| Need of cognitions (NOC2) | 1.216 |
| Need of cognitions (NOC3) | 1.078 |
| Need of cognitions (NOC4) | 1.077 |
| Product adoption (PA1) | 1.197 |
| Product adoption (PA2) | 1.259 |
| Product adoption (PA3) | 1.000 |
Table 2.
Confirmatory factor analysis (CFA).
| Construct Name | Items | Outer Loadings | Cronbach’s Alpha | CR | AVE |
|---|---|---|---|---|---|
| Autonomous environmental behavior (AEB) | AEB1 | 0.651 | 0.450 | 0.699 | 0.369 |
| AEB2 | 0.521 | ||||
| AEB3 | 0.651 | ||||
| AEB4 | 0.634 | ||||
| Customer engagement (CE) | CE1 | 0.441 | 0.645 | 0.764 | 0.355 |
| CE2 | 0.565 | ||||
| CE3 | 0.638 | ||||
| CE4 | 0.441 | ||||
| CE5 | 0.702 | ||||
| CE6 | 0.633 | ||||
| CE7 | 0.560 | ||||
| Environmental well-being (EWB) | EWB1 | 0.626 | 0.638 | 0.786 | 0.480 |
| EWB2 | 0.627 | ||||
| EWB3 | 0.751 | ||||
| EWB4 | 0.757 | ||||
| Need of cognition (NOC) | NOC1 | 0.603 | 0.512 | 0.722 | 0.369 |
| NOC2 | 0.611 | ||||
| NOC3 | 0.564 | ||||
| NOC4 | 0.727 | ||||
| Product adoption (PA) | PA1 | 0.664 | 0.528 | 0.760 | 0.516 |
| PA2 | 0.679 | ||||
| PA3 | 0.804 | ||||
| Moderating | Customer engagement × need of cognition | 0.069 | |||
| Customer engagement × product adoption | 0.052 |
Table 3.
Predictivity of the model.
| Variables | R-Square | R-Square Adjusted |
|---|---|---|
| AEB | 0.113 | 0.105 |
| CE | 0.204 | 0.264 |
| EWB | 0.022 | 0.017 |
| NOC | 0.105 | 0.101 |
| PA | 0.303 | 0.289 |
Table 4.
Hypothesis testing.
| Hypothesis | Structural Relation | Std. Deviation (STDEV) | T-Values | p-Values | Beta | Result |
|---|---|---|---|---|---|---|
| H1 | Autonomous environmental benefits -> customer-initiated engagement | 0.070 | 4.658 | 0.000 | 0.024 | Accepted |
| H2 | Customer engagement -> product adoption | 0.071 | 2.596 | 0.009 | 0.017 | Accepted |
| H3 | Autonomous environmental benefits -> environmental well-being | 0.086 | 1.716 | 0.086 | 0.016 | Rejected |
| H4 | Environmental well-being -> product adoption | 0.084 | 3.888 | 0.000 | 0.012 | Accepted |
| Indirect effects | ||||||
| H5 | Environmental well-being -> autonomous environmental benefits, Product Adoption | 0.086 | 2.242 | 0.025 | 0.008 | Accepted |
| H6 | Customer engagement × need of cognition -> product adoption | 0.053 | 0.339 | 0.735 | -0.005 | Rejected |
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MDPI and ACS Style
Zaman, S.I.; Jadoon, S.T.; Khan, S.A. Green Algorithms: The Impact of Artificial Intelligence on Environmental Sustainability. Eng. Proc. 2024, 76, 40. https://doi.org/10.3390/engproc2024076040
AMA Style
Zaman SI, Jadoon ST, Khan SA. Green Algorithms: The Impact of Artificial Intelligence on Environmental Sustainability. Engineering Proceedings. 2024; 76(1):40. https://doi.org/10.3390/engproc2024076040
Chicago/Turabian StyleZaman, Syed Imran, Shafaq Tariq Jadoon, and Sharfuddin Ahmed Khan. 2024. "Green Algorithms: The Impact of Artificial Intelligence on Environmental Sustainability" Engineering Proceedings 76, no. 1: 40. https://doi.org/10.3390/engproc2024076040
APA StyleZaman, S. I., Jadoon, S. T., & Khan, S. A. (2024). Green Algorithms: The Impact of Artificial Intelligence on Environmental Sustainability. Engineering Proceedings, 76(1), 40. https://doi.org/10.3390/engproc2024076040
