Next Article in Journal
Soil Arsenic Toxicity Impact on the Growth and C-Assimilation of Eucalyptus nitens
Previous Article in Journal
Local Context of Climate Change Adaptation in the South-Western Coastal Region of Bangladesh
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 8
10.3390/su15086666
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Do Consumers Intend to Use Indoor Smart Farm Restaurants for a Sustainable Future? The Influence of Cognitive Drivers on Behavioral Intentions

by
Kyuhyeon Joo
and
Jinsoo Hwang
*
The College of Hospitality and Tourism Management, Sejong University, Seoul 143747, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6666; https://doi.org/10.3390/su15086666
Submission received: 1 March 2023 / Revised: 13 April 2023 / Accepted: 13 April 2023 / Published: 14 April 2023
(This article belongs to the Section Tourism, Culture, and Heritage)
Browse Figure
Review Reports Versions Notes

Abstract

:
Smart farms are eco-friendly and sustainable agriculture practices that also play a crucial role in the foodservice industry. This study investigated cognitive drivers, which included biospheric value, environmental concern, problem awareness, and ascription of responsibility, in order to form consumers’ behavioral intentions in the context of indoor smart farm restaurants. The current study also investigated the differences among the four sub-dimensions of cognitive drivers, which are based on the respondents’ demographic factors. This study was performed using data from 310 participants. The study conducted multiple linear regression to test the causal relationships and t-test and one-way ANOVA to test the demographic differences. The results of the data analysis revealed that all four sub-dimensions of the cognitive drivers aid in regard to increasing behavioral intentions. Furthermore, the data analysis results showed that age and marital status were associated with differences in biospheric value, and gender was associated with differences in environmental concern and problem awareness. This study empirically identified the direct effect of cognitive drivers on consumers’ pro-environmental behavior and their demographic differences, and it also presents practical suggestions from the perspective of green marketing.

1. Introduction

Sustainable agriculture can help reduce the environmental pollution that is caused by conventional agriculture practices, such as excessive water and chemical inputs, soil degradation, and habitat destruction [1,2]. Smart farms can improve the efficiency and effectiveness of sustainable agriculture practices by using precision agriculture techniques in order to optimize resource use and crop management [3,4]. Moreover, smart farms reduce the carbon footprint compared to conventional agriculture, which causes climate change due to soil carbon emissions [5,6]. Some restaurants utilize smart farms in their operations in order to grow fresh produce on-site, which reduces their carbon footprint and ensures a steady supply of fresh produce [7,8]. These cases, which are called indoor smart farm restaurants (hereafter ISFRs), emerged in Asia and Europe [9]. For instance, a restaurant brand in Hong Kong, which is called Interval, launched an ISFR by cooperating with the smart farm firm Farmacy HK [10]. It reported that the customers could see the chefs handpick salad greens and edible herbs from the smart farms and garnish their dishes. Joo et al. [9] studied the ISFR’s potential consumers, and they emphasized norm activation and self-interested motives in regard to enhancing the consumers’ behavioral intentions. However, there is still insufficient research about consumer behavior in the field of ISFRs.
Pro-environmental behavior is more importantly fostered based on diverse cognitive drivers [11,12,13]. Cognitive drivers are psychological factors that motivate individuals in regard to decision-making, and these drivers influence the individuals’ behaviors that are related to sustainability [14,15,16]. The extant studies of factors such as biospheric value (BV), environmental concern (EC), problem awareness (PA), and ascribed responsibility (AR) provide evidence for consumers’ pro-environmental behavior [17,18,19]. For instance, Liang et al. [20] also proved that consumers’ biospheric value plays a significant role in forming intentions to purchase green apparel products. Siyal et al. [21] also found that environmental concerns had a significant impact on intentions to purchase eco-friendly organic food products. Li et al. [22] also proved that when consumers are aware of environmental issues, they are more likely to purchase green products. Joshi and Rahman [23] demonstrated that consumers’ drive for environmental responsibility had a positive influence on sustainable purchase behavior. There are still no studies that examine them in the ISFR field despite the importance of cognitive drivers. The present study focuses on the four cognitive drivers in regard to investigating the behavioral intentions of potential ISFR consumers. In addition, research that overlooks the demographic factors cannot successfully assess pro-environmental behavior [24]. For instance, Eagly [25] stated that females are more aware of environmental issues. Previous studies also state that older consumers are more likely to have a high level of ecological concerns and a higher tendency to exhibit more pro-environmental behavior than younger consumers [26]. Nonetheless, research on the differences in the cognitive drivers that is based on demographic factors is insufficient. This research tried to further strengthen the prior literature by investigating the effect of differences in demographic factors on the cognitive drivers.
ISFR is not yet common worldwide including in Korea, which is where this study was conducted, so it is crucial to investigate the consumer behavior of prospective future customers of ISFR. Thus, this study focused more on the concept of cognitive drivers in order to examine the ISFR consumers’ pro-environmental behavior. The objectives of this paper are as follows. (1) Investigate the cognitive drivers as predictors of consumer behavior in the context of ISFRs, (2) identify the influence of the cognitive drivers on behavioral intentions, and (3) investigate differences in the cognitive drivers, which are based on the demographic factors.

2. Literature Review

2.1. Indoor Smart Farm Restaurants (ISFRs)

According to the standard population projection, agriculture productivity has to be increased by more than 50% in the near future due to the growing population and climate change [27,28]. Vertical farming, a method of hydroponically cultivating crops in stacked trays, contributed to increasing agricultural productivity per area [29,30]. Furthermore, the application of IoT (Internet of Things)-based automation systems to vertical farming presented dramatically increasing production efficiency [9,31]. It is called “Smart Farming”, which can automatically control various aspects of the environment, such as light, water supply, and air condition, to enhance productivity and efficiency in agriculture [1,3,4]. They also have the potential to enhance food security and sustainability by enabling the real-time monitoring of crop growth, soil moisture, and other environmental factors [4,32]. Huang et al. [5] emphasized the eco-friendly role of smart farms as a solution to the challenges of climate change and environmental degradation by reducing carbon emissions and preserving natural resources. Smart farms reduce the carbon footprint compared to conventional agriculture, which causes climate change due to soil carbon emissions [5,6].
Some restaurants use smart farms in their operations in order to grow fresh produce on-site, which reduces their carbon footprint and ensures a steady supply of fresh produce [7,8], and Joo et al. [9] defined this type of restaurant that uses smart farming to produce ingredients as an ISFR. They mainly have been emerging in Asia and Europe [9]. For instance, a restaurant brand in Hong Kong, which is called Interval, cooperated with a smart farm firm in Hong Kong, which is a representative case of an ISFR [10]. The restaurant Beba, which is in Germany, also set up smart farms near the customer tables and used the greens from the smart farms [8]. Joo et al. [9] studied the ISFR consumers’ decision-making process for the first time. They integrated the theory of planned behavior (TPB) and the norm activation theory (NAM), and they identified the moderating role of age. However, they could not capture the effect of the cognitive drivers, such as biospheric value and environmental concern, and there were also limited investigations into the impact of the demographic factors.

2.2. Cognitive Drivers

Cognitive refers to mental processes that are related to thinking and perceiving, which are essential for making decisions and understanding the world around us [33,34,35]. Cognitive drivers refer to psychological factors that motivate individuals in regard to decision making that is related to a specific behavior, such as pro-environmental consumption [14,16]. Previous studies emphasized the important role of cognitive drivers in regard to forming the consumers’ pro-environmental decision-making process [11,13,36]. The extant studies of factors, such as biospheric value, environmental concern, problem awareness, and ascribed responsibility provide evidence for the consumers’ pro-environmental behavior [18,19,37]. First, biospheric value is the extent that individuals see themselves as being connected to and dependent on the natural environment [16]. It also involves the individuals’ value that nature should be preserved, which can influence their willingness to engage in environmental protection [37]. Second, environmental concern refers to the degree that individuals worry about environmental problems [15]. It reflects an individual’s moral obligation in regard to protecting the environment sustainably and its natural resources [38]. Third, problem awareness is the extent that individuals recognize and understand environmental pollution problems [14]. It is the recognition and understanding of these types of issues and their potential impact on society and the natural world [39,40]. Finally, ascription of responsibility is a psychological concept that refers to the extent that individuals believe that they are responsible for addressing environmental issues [41,42]. It involves being responsible for environmental problems and their willingness to act in order to address them [43]. The present study adapted the four constructs of cognitive drivers in order to predict consumer behavior in the context of ISFRs.

2.3. The Effects of Cognitive Drivers on Behavioral Intentions

Previous studies applied these four cognitive drivers in order to investigate consumers’ pro-environmental behavior. For instance, Han et al. [13] identified that the four cognitive drivers form consumers’ moral norms and that they lead to environmental pro-environmental behavioral intentions in the cruise context. Choe et al. [11] proved that the four cognitive drivers lead to personal norms, which positively affect behavioral intentions in the field of environmentally friendly edible insect restaurants. In addition, the norm activation theory (NAM) [41], the value-belief-norm theory (VBN) [44], and the theory of green purchase behavior (TGPB) [45] also support the crucial role of cognitive drivers in the process of forming behavioral intentions.
The cognitive drivers can more importantly directly influence consumers’ pro-environmental behavior. First, biospheric value is significantly associated with pro-environmental behaviors [16]. Biospheric value makes individuals see themselves as being connected to and dependent on the natural environment, so it can influence their willingness to engage in green consumption [37]. Biospheric value has also been found to be a significant predictor of pro-environmental behaviors because the value reflects a concern for the natural environment and a desire to protect it [46]. Liang et al. [20] also proved that consumers’ biospheric value plays a significant role in forming intentions to purchase green apparel products. It can be inferred that potential consumers’ biospheric value would positively affect behavioral intentions toward eco-friendly ISFRs.
Hypothesis 1 (H1).
Biospheric value positively influences behavioral intentions.
Second, environmental concern is the degree to which individuals are worried about environmental issues, so it is another essential cognitive driver that influences green consumption behavior [47]. Hines et al. [48] discovered that concern for the environment was a significant predictor of pro-environmental behaviors. Hartmann and Apaolaza-Ibáñez [49] also proved the positive effect of environmental concern on purchase intentions in regard to using green energy brands. Siyal et al. [21] also found that environmental concerns had a significant impact on intentions to purchase eco-friendly organic food products. Based on the discussions above, potential consumers’ environmental concerns can play a significant role in forming behavioral intentions in the context of ISFRs.
Hypothesis 2 (H2).
Environmental concern positively influences behavioral intentions.
Third, problem awareness about environmental pollution is also positively associated with pro-environmental behavior [14]. Problem awareness makes consumers understand these types of issues and their potential impact on society and the natural world [39,40], so they would be willing to choose a better way for sustainable environmental protection. Li et al. [22] also proved that when consumers are aware of environmental issues, they are more likely to purchase green products. It can be inferred that potential consumers’ problem awareness can foster pro-environmental behavior. Thus, ISFR consumers’ problem awareness would influence behavioral intentions.
Hypothesis 3 (H3).
Problem awareness positively influences behavioral intentions.
Lastly, ascription of responsibility is the degree to which individuals feel responsible for environmental problems and their willingness to act to address them, which can also affect green consumption behavior [36,43]. For instance, Dagher and Itani [50] stated that consumers are more likely to engage in green consumption when perceiving environmental responsibility. Joshi and Rahman [23] demonstrated that consumers’ drive for environmental responsibility had a positive influence on sustainable purchase behavior. Alssad et al. [51] also identified a significant effect of ascription of responsibility on pro-environmental behavioral intentions in the context of social media. Thus, ISFR consumers’ ascription of responsibility can play a positive role in forming their behavioral intentions.
Hypothesis 4 (H4).
Ascription of responsibility positively influences behavioral intentions.

2.4. Differences in Cognitive Drivers Based on Demographic Factors

Demographic factors, which include gender, age, marital status, education level, and monthly income level, are regarded as crucial elements in green consumer research [52,53,54]. For instance, Kim et al. [55] found differences in consumers’ internal environmental locus of control level according to gender, age, marital status, education level, and monthly income. The present study also tried to investigate differences in the cognitive drivers based on demographic factors, which is similar to their study, so there is also sufficient theoretical evidence for demographic differences. Eagly [25] studied gender differences in social behavior, and the study showed that females are more aware of environmental issues. The extent literature also states that older consumers are more likely to have a high level of ecological concerns and a higher tendency to exhibit more pro-environmental behavior than younger consumers [24,26]. Jorgensen and Stedman [56] also demonstrated that married individuals are more likely to engage in environmentally responsible behaviors than single individuals. Gatersleben et al. [57] identified that consumers with high education and income levels are more concerned about environmental problems. These studies support the idea that there are differences in consumers’ values and concerns about the environment, awareness about environmental pollution, and responsibility based on demographic factors. Thus, the present study proposes the hypothesis below.
Hypothesis 5 (H5).
There are differences in the cognitive drivers based on demographic factors.

2.5. Proposed Research Model

The conceptual model, which is illustrated in Figure 1, is presented in this study according to the proposed hypotheses.

3. Methodology

3.1. Measures

The measurement items that are used in this study are based on previous research. The four constructs of cognitive drivers, which include biospheric value, environmental concern, problem awareness, and ascription of responsibility, were measured using twelve items that were drawn from Choe et al. [11], Han et al. [13], Liang et al. [20], Siyal et al. [21], Li et al. [22], and Joshi and Rahman [23]. Behavioral intentions were measured using 3 items, which were drawn from Ajzen [58] and Joo et al. [9]. The study made some modifications to the original items in order to better fit the context of ISFRs, and they were carefully reviewed by faculty members and survey experts. The study measured all 15 items using a 7-point Likert scale, which ranged from (1) strongly disagree to (7) strongly agree. Finally, the present study collected information about demographic factors, which included gender, age, marital status, education level, and monthly income.

3.2. Data Collection and Analysis

The data were collected using the largest survey company in Korea, which includes over 1.5 million panelists. A total of 5792 panelists who had eaten out within the last six months were sent an email survey by the company. The panelists were shown a video and article that fully explained ISFRs and their eco-friendly aspect before the survey. A token of gratitude of approximately USD 1 was given to the panelists after they completed the survey. The data that were collected included 330 panelists, and the study used 310 panelists after removing 20 multivariate outliers. The study conducted multiple linear regression, t-test, and one-way ANOVA to test the suggested hypotheses using SPSS 22.0 software.

4. Data Analysis

4.1. Profile of Respondents

Table 1 presents the profile of the respondents (n = 310). Among the respondents, 48.7% were males (n = 147) and 51.3% were females (n = 155). The average age of the respondents was 36.86 years. The respondents with a monthly household income of USD 2001 to USD 3000 constituted the largest group, which accounted for 28.8% (n = 87). The majority of the respondents, who accounted for 52%, were single (n = 157), and 62.9% of them have a bachelor’s degree (n = 190).

4.2. Principal Component Analysis

This study conducted a principal component analysis in order to assess the sub-dimensions of the cognitive drivers, and the results are presented in Table 2. The study discovered that all four cognitive drivers were unidimensional with eigenvalues that exceeded 1.0, which indicates their validity. The validity was also confirmed by a high Kaiser–Meyer–Olkin, which will hereafter be referred to as KMO, value of 0.924 and a statistically significant Bartlett’s test of sphericity at p < 0.001. The factor loadings for all values were higher than 0.7. The internal consistency was appropriate, which was confirmed by Cronbach’s alpha values being greater than 0.7 for each construct. The behavioral intentions were also assessed by conducting a principal component analysis, and the results are presented in Table 3. The validity was also confirmed by a high KMO value of 0.75 and a statistically significant Bartlett’s test of sphericity at p < 0.001. The factor loadings for all the values and Cronbach’s alpha for each construct were higher than 0.9.

4.3. Result of the Convergent and Discriminant Validities Test

Table 4 indicates Pearson correlation coefficients and the convergent and discriminant validities test. The composite reliabilities of the seven proposed concepts ranged from 0.756 to 0.954, the average variance extracted value of each concept ranged from 0.508 to 0.874, and the correlation coefficient between all concepts was lower than the root square value of the average variance extracted values. The results indicated that internal consistency (CR > 0.7), convergent validity (AVE > 0.5), and discriminant validity (correlation < √AVE) satisfied the cutoff.

4.4. Result of Regression: The Effect of the Cognitive Drivers on Behavioral Intentions

Table 5 presents the result of the regression analysis conducted in order to test the hypotheses of the effects of the cognitive drivers on behavioral intentions. The results revealed that biospheric value (β = 0.376, t = 7.954, and p < 0.001), ascription of responsibility (β = 0.277, t = 5.863, and p < 0.001), environmental concern (β = 0.253, t = 5.353, and p < 0.001), and problem awareness (β = 0.189, t = 4.003, and p < 0.001) positively affect behavioral intentions. That is, all four constructs of cognitive drivers played significant roles in forming behavioral intentions in the context of ISFRs, so H1, H2, H3, and H4 were supported.

4.5. Results of the t-Tests and the One-Way ANOVA: Differences in Demographic Factors on the Cognitive Drivers

Table 6 presents the results of the t-tests and the ANOVA, which were conducted in order to test the hypothesis of the differences in the cognitive drivers, which were based on demographic factors. A p-value that was less than 0.1 was used as the cutoff for significance in the present study, which was based on extant studies [55,59,60,61]. The results of the t-tests showed that there were significant differences in environmental concern (t = −2.284 and p < 0.05) and problem awareness (t = −2.880 and p < 0.01) based on gender. It indicated that females had higher environmental concern and problem awareness than males did. The results of the ANOVA showed that there were significant differences in biospheric value based on age (F = 2.862 and p < 0.05) and marital status (F = 2.578 and p < 0.1). The post hoc tests indicated that individuals in their 20s had higher biospheric value than individuals in their 30s and individuals in their 50s or older. Additionally, married individuals had higher biospheric value than single individuals. However, the differences in ascribed responsibility based on demographic factors were found to be insignificant. Therefore, H5 was only partially supported.

5. Discussions and Conclusions

5.1. Theoretical Implications

First, the present study successfully investigated cognitive drivers in the context of ISFRs. Cognitive drivers are the psychological factors that motivate individuals to decide about a specific behavior, such as pro-environmental consumption [14,16]. This study adopted four constructs of cognitive drivers, which included biospheric value, environmental concern, problem awareness, and ascription of responsibility. The cognitive drivers were regarded as predictors of norms that play a significant role in forming behavioral intentions based on previous theories such as the NAM [41], the VBN [44], and the TGPB [45]. While recent studies in the green research field adopted these models [9,11,13], this study hypothesized the direct effects of cognitive drivers on behavioral intentions based on the previous literature e.g., [14,36,46,49]. The results of the regression analysis revealed that all four cognitive drivers significantly influence behavioral intentions. Previous theories limited the roles of cognitive drivers as predictors of norms, but this study presents a theoretical extension showing the significant roles of cognitive drivers in arousing behavioral intentions.
Second, this study also presents theoretical extensions that investigated differences in the cognitive drivers, which are based on demographic factors. The study focused on five demographic factors, such as gender, age, marital status, education level, and monthly income level, which are considered to be crucial elements in green consumer research [52,53,54]. The study hypothesized that there are differences in the cognitive drivers according to demographic factors, which is based on the previous literature [20,44,45,46,47]. The results of the analysis revealed that there were partially significant differences in the cognitive drivers according to gender, age, and marital status. These findings are consistent with the studies on green consumers, such as those by Eagly [25], Jorgensen and Stedman [56], Roberts [24], and Vining and Ebreo [26]. Whereas previous studies focusing on cognitive drivers overlooked the differences in them according to demographic factors e.g., [11,13,20,21], the study successfully found differences in the cognitive drivers according to demographic factors.
However, there were no significant differences in the cognitive drivers according to education level and monthly income, which is not in line with the study by Gatersleben et al. [57]. There may not be a significant difference in cognitive abilities based on these two factors, which is due to the education and income levels being significantly increased over the past two decades. Nevertheless, Kim et al. [55] found differences in internal environmental locus of control based on the level of education and income in the foodservice context. The concept of locus of control is the extent to which people perceive that they control their behavior [62]. This means that differences in individuals’ green characteristics according to demographic factors can be different according to the locus of behavioral control or the cognitive drivers. These results and discussions provide a theoretical extension to the field of pro-environmental consumer behavior.

5.2. Practical Suggestions

First, ISFR marketers can promote the eco-friendly roles of smart farms in order to enhance consumers’ behavioral intentions. The study identified that biospheric value and environmental concern positively affect behavioral intentions. Conventional agriculture’s harm to the biosphere is due to soil carbon emissions [5,6], so consumers may not generally perceive it very well. Marketers can promote the eco-friendliness of smart farms for a sustainable future by emphasizing these environmental concerns to consumers. This publicity can serve as the first step towards raising awareness of ISFRs among consumers.
Second, marketers should establish promotions that make foodservice consumers aware of environmental issues as well as have responsibility for them. This study demonstrated that problem awareness and ascription of responsibility have positive effects on behavioral intentions. Restaurants can cause environmental degradation because they use inefficient energy/water use practices and emit carbon dioxide [63,64]. ISFRs strive to minimize environmental degradation via a sustainable agricultural system. Marketers can launch promotional campaigns that encourage consumers to participate in environmental protection. For instance, consumers can receive a discount voucher for their next visit to an ISFR if they post pictures with specific hashtags on their social media accounts after their visit. Specific hashtags can be designated, such as #environmental_protection, #sustainable_dining, and #green_restaurant.
Finally, a customer-targeting strategy can be established that is based on demographic factors. The results of the t-test indicated that female consumers have higher environmental concern and problem awareness than male consumers do. The results of the ANOVA indicate that consumers who are in their 50 s have higher biospheric value than consumers who are in their 20 s and 30 s, and married consumers have higher biospheric value than single consumers. It can be inferred that the optimal target customers for ISFRs are females, more than 50 years old, and married. For instance, ISFRs may not be appropriate for fast-food restaurants that have small table spaces and rely on self-service. The fast-food ISFR case involving Good Stuff Eatery canceled its ISFR after five months of launching, which was due to sluggish business performance [65]. An ISFR will be appropriate for family restaurant types with large table spaces and a full-service basis, and they should consider gender, age, and marital status. If an ISFR launch plan is established based on these types of optimized targets, it will evoke consumers’ cognitive drivers and lead to their behavioral intention.

5.3. Limitations and Future Research

First, the study utilized data that were measured from one survey, which may have a common method bias [66]. Future research should consider data collection methods that minimize the possibility of this issue. Second, the generalizability of the findings is limited because the study collected respondents only from Korea. There may be variations in individuals’ cognition and behavior based on cultural dimensions [67], so it is recommended that further research incorporate these cross-cultural differences as a moderator. Third, word-of-mouth intentions and willingness to pay more are also regarded as crucial behaviors in the green research field [55,68,69]. A comprehensive investigation that includes these two behaviors as dependent variables and investigates their predictors in the context of ISFRs is suggested. Fourth, the study overlooked the moderating role of consumers’ demographic factors. For example, consumers’ decision-making processes of pro-environmental behavior can be moderated by their gender or age [9,70]. It implies that future research should consider the moderating role of demographic factors. Lastly, the study used a sufficient panelist size grounded on previous studies [71,72], but the study could not test whether panelists fully understood the research background. It suggests that future research can use attention-check questions about the research background after panelists read/watch the articles/videos. The panelists for this study also did not represent actual visitors of ISFRs, because ISFRs have not yet been fully commercialized in Korea. The study suggests future research that targets actual visitors of ISFRs and considers their behavioral descriptors (e.g., restaurant attendance, average expenditure, etc.) as control variables or difference factors.

Author Contributions

Conceptualization, K.J. and J.H.; methodology, K.J. and J.H.; writing—original draft preparation, K.J.; writing—review and editing, K.J. and J.H.; supervision, J.H. 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

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cáceres, G.; Millán, P.; Pereira, M.; Lozano, D. Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture. Agronomy 2021, 11, 1810. [Google Scholar] [CrossRef]
  2. Foley, J.A.; Ramankutty, N.; Brauman, K.A.; Cassidy, E.S.; Gerber, J.S.; Johnston, M.; Mueller, N.D.; O’connell, C.; Ray, D.K.; West, P.C.; et al. Solutions for a cultivated planet. Nature 2021, 478, 337–342. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358–371. [Google Scholar] [CrossRef]
  4. Kanjilal, D.; Singh, D.; Reddy, R.; Mathew, J. Smart farm: Extending automation to the farm level. Int. J. Sci. Technol. Res. 2014, 3, 109–113. [Google Scholar]
  5. Huang, Y.; Tao, B.; Lal, R.; Lorenz, K.; Jacinthe, P.A.; Shrestha, R.K.; Bai, S.; Singh, M.P.; Lindseay, L.E.; Ren, W. A global synthesis of biochar’s sustainability in climate-smart agriculture-Evidence from field and laboratory experiments. Renew. Sustain. Energy Rev. 2023, 172, 113042. [Google Scholar] [CrossRef]
  6. Panchasara, H.; Samrat, N.H.; Islam, N. Greenhouse gas emissions trends and mitigation measures in Australian agriculture sector—A review. Agriculture 2021, 11, 85. [Google Scholar] [CrossRef]
  7. The Korea Herald. Future of Agriculture Grows Under Seoul’s Subway Stations. 2021. Available online: http://www.koreaherald.com/view.php?ud=20210104001032 (accessed on 15 February 2023).
  8. Wired. Vertical Farms Nailed Tiny Salads. Now They Need to Feed the World. 2021. Available online: https://www.wired.co.uk/article/vertical-farms (accessed on 15 February 2023).
  9. Joo, K.; Lee, J.; Hwang, J. NAM and TPB Approach to Consumers’ Decision-Making Framework in the Context of Indoor Smart Farm Restaurants. Int. J. Environ. Res. Public Health 2022, 19, 14604. [Google Scholar] [CrossRef]
  10. Issuu. DINING from Southside May 2021 by Hong Kong Living Ltd. 2021. Available online: https://issuu.com/saikung/docs/southside_may_2021/s/12209765 (accessed on 14 February 2023).
  11. Choe, J.Y.J.; Kim, J.J.; Hwang, J. The environmentally friendly role of edible insect restaurants in the tourism industry: Applying an extended theory of planned behavior. Int. J. Contemp. Hosp. Manag. 2020, 32, 3581–3600. [Google Scholar] [CrossRef]
  12. Fornara, F.; Pattitoni, P.; Mura, M.; Strazzera, E. Predicting intention to improve household energy efficiency: The role of value-belief-norm theory, normative and informational influence, and specific attitude. J. Environ. Psychol. 2016, 45, 1–10. [Google Scholar] [CrossRef]
  13. Han, H.; Olya, H.G.; Kim, J.; Kim, W. Model of sustainable behavior: Assessing cognitive, emotional and normative influence in the cruise context. Bus. Strategy Environ. 2018, 27, 789–800. [Google Scholar] [CrossRef]
  14. Bamberg, S.; Möser, G. Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. J. Environ. Psychol. 2007, 27, 14–25. [Google Scholar] [CrossRef]
  15. Milfont, T.L.; Sibley, C.G. The Big Five personality traits and environmental engagement: Associations at the individual and societal level. J. Environ. Psychol. 2012, 32, 187–195. [Google Scholar] [CrossRef]
  16. Stern, P.C.; Dietz, T.; Abel, T.; Guagnano, G.A.; Kalof, L. A value-belief-norm theory of support for social movements: The case of environmentalism. Hum. Ecol. Rev. 1999, 6, 81–97. [Google Scholar]
  17. Chan, E.S.W.; Hon, A.H.Y.; Chan, W.; Okumus, F. What drives employees’ intentions to implement green practices in hotels? The role of knowledge, awareness, concern and ecological behaviour. Int. J. Hosp. Manag. 2014, 40, 20–28. [Google Scholar] [CrossRef]
  18. De Groot, J.I.M.; Steg, L. Value orientations to explain beliefs related to environmental significant behavior: How to measure egoistic, altruistic, and biospheric value orientations. Environ. Behav. 2008, 40, 330–354. [Google Scholar] [CrossRef]
  19. Zimmer, M.R.; Stafford, T.F.; Stafford, M.R. Green issues: Dimensions of environmental concern. J. Bus. Res. 1994, 30, 63–74. [Google Scholar] [CrossRef]
  20. Liang, J.; Li, J.; Lei, Q. Exploring the Influence of Environmental Values on Green Consumption Behavior of Apparel: A Chain Multiple Mediation Model among Chinese Generation Z. Sustainability 2022, 14, 12850. [Google Scholar] [CrossRef]
  21. Siyal, S.; Ahmed, M.J.; Ahmad, R.; Khan, B.S.; Xin, C. Factors Influencing Green Purchase Intention: Moderating Role of Green Brand Knowledge. Int. J. Environ. Res. Public Health 2021, 18, 10762. [Google Scholar] [CrossRef]
  22. Li, H.; Haq, I.U.; Nadeem, H.; Albasher, G.; Alqatani, W.; Nawaz, A.; Hameed, J. How environmental awareness relates to green purchase intentions can affect brand evangelism? Altruism and environmental consciousness as mediators. Rev. Argent. De Clin. Psicol. 2020, 29, 811–825. [Google Scholar]
  23. Joshi, Y.; Rahman, Z. Consumers’ sustainable purchase behaviour: Modeling the impact of psychological factors. Ecol. Econ. 2019, 159, 235–243. [Google Scholar] [CrossRef]
  24. Roberts, J.A. Green consumers in the 1990s: Profile and implications for advertising. J. Bus. Res. 1996, 36, 217–231. [Google Scholar] [CrossRef]
  25. Eagly, A.H. Sex Differences in Social Behavior: A Social-Role Interpretation; Psychology Press: New York, NY, USA, 1978. [Google Scholar]
  26. Vining, J.; Ebreo, A. What makes a recycler? A comparison of recyclers and nonrecyclers. Environ. Behav. 1990, 22, 55–73. [Google Scholar] [CrossRef]
  27. FAO. Looking at Edible Insects from a Food Safety Perspective, Challenges and Opportunities for the Sector. 2021. Available online: https://doi.org/10.4060/cb4094en (accessed on 11 January 2023). [CrossRef]
  28. Tal, A. Making conventional agriculture environmentally friendly: Moving beyond the glorification of organic agriculture and the demonization of conventional agriculture. Sustainability 2018, 10, 1078. [Google Scholar] [CrossRef] [Green Version]
  29. Al-Chalabi, M. Vertical farming: Skyscraper sustainability? Sustain. Cities Soc. 2015, 18, 74–77. [Google Scholar] [CrossRef]
  30. Banerjee, C.; Adenaeuer, L. Up, up and away! The economics of vertical farming. J. Agric. Stud. 2014, 2, 40–60. [Google Scholar] [CrossRef]
  31. Musa, S.F.P.D.; Basir, K.H. Smart farming: Towards a sustainable agri-food system. Br. Food J. 2021, 123, 3085–3099. [Google Scholar] [CrossRef]
  32. Huang, Y.C.; Wu, H.C.; Huang, H.Y.; Lu, C.H. The role of smart farming in food security and sustainability: An empirical investigation of perceived benefits and challenges. Sustainability 2019, 11, 2251. [Google Scholar]
  33. Butterfield, E.C.; Dickerson, D.J. Cognitive theory and mental development. Int. Rev. Res. Ment. Retard. 1976, 8, 105–137. [Google Scholar]
  34. Hunt, E. Cognitive science: Definition, status, and questions. Annu. Rev. Psychol. 1989, 40, 603–629. [Google Scholar] [CrossRef]
  35. Sternberg, R.J. Cognitive Psychology; Harcourt Brace College Publishers: Orlando, FL, USA, 1996. [Google Scholar]
  36. Schultz, P.W.; Gouveia, V.V.; Cameron, L.D.; Tankha, G.; Schmuck, P.; Franěk, M. Values and their relationship to environmental concern and conservation behavior. J. Cross-Cult. Psychol. 2005, 36, 457–475. [Google Scholar] [CrossRef]
  37. Thøgersen, J. How may consumer policy empower consumers for sustainable lifestyles? J. Consum. Policy 2010, 33, 43–64. [Google Scholar] [CrossRef]
  38. Bissing-Olson, M.J.; Iyer, A.; Fielding, K.S.; Zacher, H. Relationships between daily affect and pro-environmental behavior at work: The moderating role of pro-environmental attitude. J. Organ. Behav. 2013, 34, 156–175. [Google Scholar] [CrossRef]
  39. Grønhøj, A.; Thøgersen, J. Like father, like son? Intergenerational transmission of values, attitudes, and behaviours in the environmental domain. J. Environ. Psychol. 2009, 29, 414–421. [Google Scholar] [CrossRef] [Green Version]
  40. McBeth, M.K.; Lybecker, D.L.; Garner, K.A. The story of good citizenship: Framing public policy in the context of duty-based versus engaged citizenship. Politics Policy 2010, 38, 1–23. [Google Scholar] [CrossRef]
  41. Schwartz, S.H. Normative influences on altruism. Adv. Exp. Soc. Psychol. 1977, 10, 221–279. [Google Scholar]
  42. Kollmuss, A.; Agyeman, J. Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environ. Educ. Res. 2002, 8, 239–260. [Google Scholar] [CrossRef] [Green Version]
  43. Stern, P.C.; Dietz, T.; Guagnano, G.A. The new ecological paradigm in social-psychological context. Environ. Behav. 1995, 27, 723–743. [Google Scholar] [CrossRef]
  44. Stern, P.C. New environmental theories: Toward a coherent theory of environmentally significant behavior. J. Soc. Issues 2000, 56, 407–424. [Google Scholar] [CrossRef]
  45. Han, H. Theory of green purchase behavior (TGPB): A new theory for sustainable consumption of green hotel and green restaurant products. Bus. Strategy Environ. 2020, 29, 2815–2828. [Google Scholar] [CrossRef]
  46. Schultz, P.W.; Zelezny, L.C. Values and proenvironmental behavior: A five-country survey. J. Cross-Cult. Psychol. 1999, 30, 465–488. [Google Scholar] [CrossRef] [Green Version]
  47. Chen, M.F.; Chang, Y.Y. Enhance green purchase intentions: The roles of green perceived value, green perceived risk, and green trust. Manag. Decis. 2013, 51, 501–520. [Google Scholar] [CrossRef]
  48. Hines, J.M.; Hungerford, H.R.; Tomera, A.N. Analysis and synthesis of research on responsible environmental behavior: A meta-analysis. J. Environ. Educ. 1987, 18, 1–8. [Google Scholar] [CrossRef]
  49. Hartmann, P.; Apaolaza-Ibáñez, V. Consumer attitude and purchase intention toward green energy brands: The roles of psychological benefits and environmental concern. J. Bus. Res. 2012, 65, 1254–1263. [Google Scholar] [CrossRef]
  50. Dagher, G.K.; Itani, O. Factors influencing green purchasing behaviour: Empirical evidence from the Lebanese consumers. J. Consum. Behav. 2014, 13, 188–195. [Google Scholar] [CrossRef] [Green Version]
  51. Alsaad, A.; Alam, M.; Lutfi, A. A sensemaking perspective on the association between social media engagement and pro-environment behavioural intention. Technol. Soc. 2023, 72, 102201. [Google Scholar] [CrossRef]
  52. Akehurst, G.; Afonso, C.; Gonçalves, H.M. Re-examining green purchase behaviour and the green consumer profile: New evidences. Manag. Decis. 2012, 50, 972–988. [Google Scholar] [CrossRef]
  53. Diamantopoulos, A.; Schlegelmilch, B.B.; Sinkovics, R.R.; Bohlen, G.M. Can socio-demographics still play a role in profiling green consumers? A review of the evidence and an empirical investigation. J. Bus. Res. 2003, 56, 465–480. [Google Scholar] [CrossRef]
  54. D’Souza, C.; Taghian, M.; Lamb, P.; Peretiatko, R. Green decisions: Demographics and consumer understanding of environmental labels. Int. J. Consum. Stud. 2007, 31, 371–376. [Google Scholar] [CrossRef]
  55. Kim, H.M.; Joo, K.; Hwang, J. Are Customers Willing to Pay More for Eco-Friendly Edible Insect Restaurants? Focusing on the Internal Environmental Locus of Control. Sustainability 2022, 14, 10075. [Google Scholar] [CrossRef]
  56. Jorgensen, B.S.; Stedman, R.C. Sense of place as an attitude: Lakeshore owners attitudes toward their properties. J. Environ. Psychol. 2001, 21, 233–248. [Google Scholar] [CrossRef]
  57. Gatersleben, B.; Steg, L.; Vlek, C. Measurement and determinants of environmentally significant consumer behavior. Environ. Behav. 2002, 34, 335–362. [Google Scholar] [CrossRef]
  58. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  59. Hwang, J.; Kim, H.M.; Joo, K.; Nawaz, M.; Moon, J. Travelers’ Perceived Value of Robot Services in the Airline Industry: Focusing on Demographic Characteristics. Sustainability 2022, 14, 15818. [Google Scholar] [CrossRef]
  60. Cleak, M.J.; Eston, R.G. Muscle soreness, swelling, stiffness and strength loss after intense eccentric exercise. Br. J. Sport. Med. 1992, 26, 267–272. [Google Scholar] [CrossRef] [Green Version]
  61. Stamler, J.S.; Loh, E.; Roddy, M.A.; Currie, K.E.; Creager, M.A. Nitric oxide regulates basal systemic and pulmonary vascular resistance in healthy humans. Circulation 1994, 89, 2035–2040. [Google Scholar] [CrossRef] [Green Version]
  62. Rotter, J.B. Generalized expectancies for internal versus external control of reinforcement. Psychol. Monogr. Gen. Appl. 1966, 80, 1–28. [Google Scholar] [CrossRef] [Green Version]
  63. Chou, C.J.; Chen, K.S.; Wang, Y.Y. Green practices in the restaurant industry from an innovation adoption perspective: Evidence from Taiwan. Int. J. Hosp. Manag. 2012, 31, 703–711. [Google Scholar] [CrossRef]
  64. Horovitz, B. Can Restaurants Go Green, Earn Green? 2008. Available online: http://www.usatoday.com/money/industries/environment/2008-05-15-green-restaurants-eco-friendly_n.htm (accessed on 25 February 2023).
  65. The Korea Bizwire. Good Stuff Eatery’s Only Store in S. Korea Considers Retreat. 2022. Available online: http://koreabizwire.com/good-stuff-eaterys-only-store-in-s-korea-considers-retreat/231951 (accessed on 25 February 2023).
  66. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  67. Hofstede, G. Cultural constraints in management theories. Acad. Manag. Exec. 1993, 7, 81–94. [Google Scholar] [CrossRef]
  68. Han, H.; Hwang, J.; Lee, M.J.; Kim, J. Word-of-mouth, buying, and sacrifice intentions for eco-cruises: Exploring the function of norm activation and value-attitude-behavior. Tour. Manag. 2019, 70, 430–443. [Google Scholar] [CrossRef]
  69. Hwang, J.; Kim, D.; Kim, J.J. How to form behavioral intentions in the field of drone food delivery services: The moderating role of the COVID-19 outbreak. Int. J. Environ. Res. Public Health 2020, 17, 9117. [Google Scholar] [CrossRef] [PubMed]
  70. Ahmad, N.; Ullah, Z.; Arshad, M.Z.; Kamran, H.; Scholz, M.; Han, H. Relationship between corporate social responsibility at the micro-level and environmental performance: The mediating role of employee pro-environmental behavior and the moderating role of gender. Sustain. Prod. Consum. 2021, 27, 1138–1148. [Google Scholar] [CrossRef]
  71. Bollen, K.A.; Stine, R.A. Direct and indirect effects: Classical and bootstrap estimates of variability. Sociol. Methodol. 1990, 20, 115–140. [Google Scholar] [CrossRef]
  72. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis; Cengage Learning: Boston, MA, USA, 2006. [Google Scholar]
Sustainability 15 06666 g001 550
Figure 1. Proposed conceptual model.
Figure 1. Proposed conceptual model.
Sustainability 15 06666 g001
Table
Table 1. Respondent profiles (n = 310).
Table 1. Respondent profiles (n = 310).
Variablesn%
Gender
 Male14748.7
 Female15551.3
Age (Mean = 36.86)
 20s8628.5
 30s9230.5
 40s9230.5
 50s3210.6
Monthly income
 Under USD 20005116.9
 USD 2001–30008728.8
 USD 3001–40006621.9
 USD 4001–50003812.6
 Over USD 50016019.9
Marital status
 Single15752.0
 Married13444.4
 Widowed/Divorced113.6
Educations level
 Less than high school diploma3110.3
 Associate degree4514.9
 Bachelor’s degree19062.9
 Graduate degree3611.9
Table
Table 2. Results of the principal component analysis for the cognitive drivers.
Table 2. Results of the principal component analysis for the cognitive drivers.
Construct and Scale ItemsFactor LoadingEigen ValueExplained VarianceCronbach’s a
Cognitive drivers
 Biospheric value (5.57 and 1.09) 3.01525.1120.960
 Please indicate to what extent the following are important as guiding principles in your life from (1) very unimportant to (7) very important.
 Preventing pollution (conserving natural resources)0.867
 Respecting the earth (harmony with other species)0.881
 Protecting the environment (preserving nature0.876
 Environmental concern (5.90 and 1.01) 2.13617.8040.903
 The balance of nature is very delicate and easily upset.0.820
 Humans are severely abusing the environment.0.858
 The earth is like a spaceship with limited room and resources.0.815
 Problem awareness (5.81 and 1.04) 2.99524.9590.950
 The foodservice industry can lead to environmental pollution, such as carbon emissions, food wastes, and disposable products.0.737
 The foodservice industry can potentially have a negative impact on global warming0.786
 The foodservice industry can lead to the exhaustion of natural resources. 0.796
 Ascription of responsibility (5.50 and 1.04) 2.63621.9630.951
 I believe that every restaurant customer is partly responsible for the environmental contaminants, such as carbon emission, food waste, and disposable products, which are caused by the foodservice industry.0.686
 I feel that every restaurant customer is jointly responsible for the environmental deteriorations that are caused by the environmental contaminants, such as carbon emissions, food waste, and disposable products, which are generated in the foodservice industry.0.733
 Every restaurant customer must take partial responsibility for the environmental problems that are caused by the environmental contaminants, such as carbon emissions, food waste, and disposable products, which are generated in the foodservice industry.0.719
KMO measure of sampling adequacy = 0.924, Bartlett’s test of sphericity p < 0.001, and total explained variance = 89.848%.
Table
Table 3. Results of the principal components analysis for behavioral intentions.
Table 3. Results of the principal components analysis for behavioral intentions.
Variables (Mean and Standard Deviation)Factor LoadingEigen ValueExplained VarianceCronbach’s α
 Behavioral intentions (5.31 and 0.98) 2.62087.3440.927
 I will visit eco-friendly ISFR when I dine out. 0.923
 I’m willing to visit eco-friendly ISFR when I dine out. 0.952
 I’m likely to visit eco-friendly ISFR when I dine out. 0.929
 KMO measure of sampling adequacy = 0.750 and Bartlett’s test of sphericity p < 0.001
Table
Table 4. The convergent and discriminant validities.
Table 4. The convergent and discriminant validities.
CRAVEBVECPAARBI
BV0.9070.7650.875
EC0.8700.6910.644 **0.831
PA0.8170.5980.671 **0.687 **0.773
AR0.7560.5080.580 **0.694 **0.688 **0.713
BI0.9540.8740.515 **0.474 **0.481 **0.469 **0.713
Notes: BV = Biospheric value, EC = Environmental concern, PA = Problem awareness, AR = Ascription of responsibility, BI = Behavioral intention, CR = Composite reliability, AVE = Average variance extracted, √AVE values are along the diagonal (boldface), and Pearson correlation coefficients are below the diagonal (** p < 0.01 two-tailed).
Table
Table 5. The results of the regression: The effect of cognitive drivers on behavioral intentions.
Table 5. The results of the regression: The effect of cognitive drivers on behavioral intentions.
Independent Variable Dependent VariableBetat-ValueVIFHypothesis
H1Biospheric value →Behavioral intentions0.3767.954 ***1.951Supported
H2Environmental concern →0.2535.353 ***2.891Supported
H3Problem awareness →0.1894.003 ***3.024Supported
H4Ascription of responsibility →0.2775.863 ***2.757Supported
ANOVA (Regression; Residual): Sum of squares (98.315; 210.685), df (4; 305), Mean square (24.579; 0.691), F-value = 35.581, and p < 0.001. Notes: *** p < 0.001, R2 = 0.318, and Adjusted R2 = 0.309.
Table
Table 6. The results of the t-tests and one-way ANOVA: differences in demographic factors on the cognitive drivers.
Table 6. The results of the t-tests and one-way ANOVA: differences in demographic factors on the cognitive drivers.
GenderMaleFemalet-Valuep-Value
Environmental concern 5.776.03−2.284 **0.023
Problem awareness5.645.97−2.880 ***0.004
Age20 s30 s40 sMore than 50F-valuep-value
Biospheric value 5.33 ab5.65 a5.625.92 b2.862 **0.037
Marital statusSingleMarriedOthersF-valuep-value
Biospheric value 5.44 a5.72 a5.762.578 *0.078
Notes: * p < 0.1, ** p < 0.05, *** p < 0.01, and the upper letters show the results of LSD (Least Significant Difference) post hoc test.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Joo, K.; Hwang, J. Do Consumers Intend to Use Indoor Smart Farm Restaurants for a Sustainable Future? The Influence of Cognitive Drivers on Behavioral Intentions. Sustainability 2023, 15, 6666. https://doi.org/10.3390/su15086666

AMA Style

Joo K, Hwang J. Do Consumers Intend to Use Indoor Smart Farm Restaurants for a Sustainable Future? The Influence of Cognitive Drivers on Behavioral Intentions. Sustainability. 2023; 15(8):6666. https://doi.org/10.3390/su15086666

Chicago/Turabian Style

Joo, Kyuhyeon, and Jinsoo Hwang. 2023. "Do Consumers Intend to Use Indoor Smart Farm Restaurants for a Sustainable Future? The Influence of Cognitive Drivers on Behavioral Intentions" Sustainability 15, no. 8: 6666. https://doi.org/10.3390/su15086666

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop