Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

symmetry S S Article Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management Ana Reyes-Menendez 1, Jose Ramon Saura 1, Pedro R. Palos-Sanchez 2,3,* and Jose Alvarez-Garcia 4 1Department of Business Economics, Faculty of Social Sciences and Law, Rey Juan Carlos University, Paseo Artilleros s/n, 28032 Madrid, Spain; ana.r[email protected] (A.R.-M.); [email protected] (J.R.S.) 2Department of Business Administration and Marketing, University of Sevilla, Av. de Ramón y Cajal, 1, 41018 Sevilla, Spain 3 Department of Business Organization, Marketing and Market Research, International University of La Rioja, Av. de la Paz, 137, 26006 Logroño, Spain 4 Department of Financial Economy and Accounting, Faculty of Business, Finance and Tourism, University of Extremadura, 10071 Cáceres, Spain; [email protected] *Correspondence: [email protected]; Tel.: +34-954-55-75-15 Received: 18 September 2018; Accepted: 22 October 2018; Published: 2 November 2018   Abstract: An increase in users’ online searches, the social concern for an efficient management of resources such as water, and the appearance of more and more digital platforms for sustainable purposes to conduct online searches lead us to reflect more on the users’ behavioral intention with respect to search engines that support sustainable projects like water management projects. Another issue to consider is the factors that determine the adoption of such search engines. In the present study, we aim to identify the factors that determine the intention to adopt a search engine, such as Lilo, that favors sustainable water management. To this end, a model based on the Theory of Planned Behavior (TPB) is proposed. The methodology used is the Structural Equation Modeling (SEM) analysis with the Analysis of Moment Structures (AMOS). The results demonstrate that individuals who intend to use a search engine are influenced by hedonic motivations, which drive their feeling of contentment with the search. Similarly, the success of search engines is found to be closely related to the ability a search engine grants to its users to generate a social or environmental impact, rather than users’ trust in what they do or in their results. However, according to our results, habit is also an important factor that has both a direct and an indirect impact on users’ behavioral intention to adopt different search engines. Keywords: internet; water; behavioral intention; TPB; social search engines; sustainable water management 1. Introduction In recent years, the amount of information available on the Internet has dramatically increased [ 1 ]. The number of people who have access to the Internet has also grown, reaching 3.8 billion active users worldwide. Considering that online multimedia information is available in the form of images, text, video or audio [ 2 ], a classification system is necessary to be able to show this information to users according to the searches they perform. To this end, search engines have been created that show a series of Internet results that are indexed and that contain relevant information related to a search [ 3 ]. Several studies have studied the criteria followed by search engines to show the information and its optimization (e.g., [ 4 ]). Of all search engines that exist today, the one with the highest number Symmetry 2018,10, 584; doi:10.3390/sym10110584 www.mdpi.com/journal/symmetry Symmetry 2018,10, 584 2 of 21 of searches in the world continues to be Google, which accounts for 78.78% of the total number of searches [3]. Google is an information search engine that supports users’ online searches. However, even though its main function is to support users in finding needed information, when a query is made in Google about the total number of searches carried out on the platform daily around the world, it is difficult to find such official data in figures. These data are not available even with Google Trends, a tool whose purpose is to show search trends in Google. Instead, Google Trends provides the data on the number of queries (in percentages), rather than figures [ 4 ]. Despite the difficulty of finding official data, it is estimated that, in 2018, around 2 trillion searches will have been conducted on Google around the world, although other pages limit that figure to several billion searches a day worldwide [ 5 ]. Overall, in 2017, about 88,000 employees worked full time for Google worldwide [6]. In recent years, natural resources have been used at an unprecedented rate, and the planet does not have time to replace what we spend. In 2015, for example, 1.5 times more resources than the planet can sustain were used [ 7 ]. In this context, sustainable management of natural resources becomes a priority [ 8 ], as we need to ensure that natural resources will also be available for future generations [ 9 ]. Water is one of such natural resources. According to several estimates, in 2025, two-thirds of the population will experience water shortages [ 10 ]. Considering that Google is a company located in California, an area prone to droughts, various initiatives on the efficient use of water have been introduced. These include several projects in Google’s data centers where they redesign and improve their cooling technologies and use non-potable water. Google.org has also founded the Climate Savers Computing Initiative that, since 2007, has pursued efficiency and sustainability standards. The company has also donated 45 million dollars for research on, among other things, relevant solutions to respond to the global challenges of water management. The aim of the present study is to identify the factors that determine user intention to adopt a search engine that promotes sustainable water management. The search engine under study, Lilo.org, gives users a drop of water for each query made. With these drops of water, projects related to sustainable management can be supported and, therefore, the management of water resources can be improved. In order to comply with the proposed objective, a model based on TPB is proposed, which is contrasted using the statistical technique SEM with AMOS. 2. Theoretical Background While it may seem, due to their being Internet-based, that search engines function in the most sustainable way, each individual search has externalities and thus can have an impact on the environment [7]. According to the official Google statement made in the blog of the multinational company [ 11 ], each search performed in the search engine is equivalent to 0.2 g of CO 2 and, for each query made, an average of 0.0003 kWh of energy (1 kJ) is consumed. This figure would be the equivalent to the energy consumed by the human body in 10 s. Translating these figures into everyday activities, the searches performed by each individual in a year would have the same impact on the environment as running a washing machine. These statements about the consumed energy were not by Google’s own initiative, but were made in response to the research published by Wissner-Gross [ 12 ] who stated that each Google search produced 7 g of CO 2 , half of the energy that a teapot consumes to warm up. After Google’s rebuttal, the author had to correct the figures, stating that the impact on the environment was 0.2 g of CO2. Despite the efforts made by the search engine to minimize the impact of its activity on the environment, Google says that every search carried out with a laptop consumes more energy than Google consumes in facilitating the search—i.e., it consumes energy and produces CO 2 when search engines are used on personal computers, no matter how much Google tries to minimize its share [ 13 ]. Symmetry 2018,10, 584 3 of 21 Therefore, it is necessary to assess the use of alternative solutions to traditional search engines that, while fulfilling the function of providing information, also compensate for the generated externalities through social projects [ 7 ]. As can be seen in Table 1, among traditional online search engines, there are numerous alternatives; however, but not all of them offset the impact generated in the environment. Among all the existing categories, we can differentiate the Social Search Engines, i.e., search engines based on creativity and innovation that calculate and compensate for externalities, since they pursue the sustainable management of companies while favoring the global sustainability of the Earth. Some examples of this type of Social Search Engine are Ecosia or Lilo [3,14]. Table 1. Types of Search Engines. Search Engine Type Description Examples Social Search Engines These search engines have social purposes in which a percentage of the income is allocated to projects related to sustainability. They enable searching for images, video, music, and web pages. Ecosia Lilo Science Search Engines These search engines allow access to scientific-technical materials through specific searches. The bibliographical production on the analyzed subject is gathered. Google Scholar Scientific Commons Sci-Hub General Search Engines These search engines are the most widely used ones and contain files stored on web servers that, with each search, offer the results of general content that is most relevant to the user. Google Yahoo Bing Safe search Engines These searchers allow safe searches for children. They do not show icon images to prevent display of inappropriate images. Safe Search for kids Local Search Engines These local search engines help to find videos, news, blogs, web pages, radio, and images. Rambler (Rusia) Goo (Japón) Baidu (China) Social Media Search Engines These search engines are created specifically to find content from social networks, including blogs, microblogs, comments, bookmarks, and videos. Some offer the possibility of creating alerts to track real-time topics of interest. Social mention Social Search TagBoard Source: Palos-Sánchez and Saura [3], An et al. (2017). Social Search Engines are those search engines that support creativity and innovation to use an important part of their resources to improve the sustainability of the planet [ 15 ]. The social purposes of these search engines range from tree planting, sustainable water management, emission of CO 2 certifications or support to general projects. Table 2shows some examples of search engines that develop actions on their own web pages to favor the efficient management of resources by supporting sustainable projects. Table 2. Description of Social Search Engines that develop sustainable projects. Social Search Engine Description Lilo A search engine that finances sustainable projects related to water. Each time a user performs a search, s/he gets a drop of water; collecting the necessary amount of drops can finance a relevant project. This search engine is focused on innovation and creativity. Ecosia A search engine that allocates 80% of the benefits it obtains to finance sustainable projects; specifically, a portion of the profits goes to planting trees. Good Search A search engine that allocates a percentage of the purchases made through the platform to over 100,000 projects. It also offers discounts on products available for purchase. Znout A search engine that purchases CO2certificates to amplify its growth and sustainability in future projects. Revenue is obtained from the performed searches. Benefind A search engine that donates €0.5 to a social project supported by innovation and creativity every time a user performs a search. It has been active since 2010. As can be seen in Table 2, as long as there is an approach to the sustainable management of resources and support for sustainable initiatives, business models used by search engines are diverse, Symmetry 2018,10, 584 4 of 21 and there are many ways to make projects focused on environmental sustainability viable in all senses [16]. 2.1. Sustainable Water Management Among all possible projects that can be supported via search engines, management of water resources is a particularly interesting domain. Water is not only essential for human survival, but is also the central element of sustainable development required for the development of economies and ecosystems [ 17 , 18 ]. Effective water management necessitates a union between human beings, the environment, and the climate system. Of note, water is only a renewable resource if it is well managed. At present, 1700 million people live in places where the use of water exceeds the natural recharge rate and, according to several estimates, in 2025, about two-thirds of the population will experience water shortages. Despite the importance of water for the survival of living beings, at present, the management of water resources is not sustainable from the environmental perspective [ 19 , 20 ]. Two of the indicators that demonstrate this are water withdrawal and water treatment [21]. First, water withdrawal refers to the extraction of water transported for its use, temporarily or permanently, to another place. Among the most common uses of water are the public water supply, irrigation, industrial processes, or cooling of power plants. Along with the river discharge and climate changes, water withdrawal is the main cause of water shortage and poses a great threat to the populations living in places where the water is extracted [22]. This indicator is measured per m 3 per capita. Owing to several recently implemented policies, this measure has been considerably reduced in most countries (see Table 3). Table 3. Water withdrawals. Location 2007 2008 2009 2010 2011 2012 2013 2014 2015 Mexico 78,949.6 79,752.3 80,587.0 80,213.4 81,588.1 82,733.7 81,651.2 84,928.8 85,664.2 Russia 74,633.0 74,354.0 69,915.0 72,685.0 68,652.0 66,296.0 65,104.0 64,807.0 62,163.0 Australia 14,613.0 13,842.0 13,702.0 16,351.0 20,133.0 19,364.0 18,222.0 Poland 12,027.0 11,365.0 11,517.0 11,645.0 11,911.0 11,478.0 11,242.0 11,308.5 11,093.5 Greece 9471.6 9934.6 9934.9 9924.5 9916.3 9907.7 Costa Rica 384.5 486.4 853.3 1066.2 1246.1 1347.4 1656.2 1990.3 Estonia 1834.3 1605.3 1388.0 1842.0 1873.9 1631.0 1747.8 1724.1 1615.3 Czech Republic 1970.0 1988.0 1948.0 1950.0 1886.0 1840.0 1650.0 1650.0 1603.1 Israel 1689.0 1595.0 1313.0 1340.0 1266.0 1318.0 1296.0 1271.0 1145.0 Slovenia 935.0 1040.0 943.0 925.0 851.0 781.1 892.5 977.4 895.1 Source: OECD [23]. The second major problem in sustainable water management is the treatment of wastewater [ 24 ]. By definition, waste water treatment should neutralize the negative impact of wastewater on the environment and favor the continuation of the water cycle. In sum, we need be aware of the causes that can, according to available estimates, lead two-thirds of the population to suffer from water shortages in the future. In addition, efficient solutions should also be sought and implemented. In this context, support and financing of projects related to sustainable water management, such as social seekers, highly assist this purpose, as they support projects of efficient water management [25,26]. 2.2. Lilo: Sustainable Water Management Through Donation of Drops The Lilo search engine is a search engine that finances, through the searches made on the platform, social and environmental projects related to water. Each time a user performs a search, s/he user obtains drops of water that allow him/her to decide to which projects s/he will allocate the generated money. Symmetry 2018,10, 584 5 of 21 Table 4shows some key data about the Lilo search engine. Lilo was created by three young individuals in 2014. Its headquarters are in Paris, France. It currently has 136,279 monthly searches and 2750 users. The total income generated thus far amounts to €60,675. Table 4. Overview of Lilo. Lilo.org Description Foundation 2014 Headquarters Paris/France Industry Internet, Social Business Product and Services Internet Search Services Short Description Independent Non-profit website Partners 130 businesses with social projects URL www.lilo.org Employees 3 Total Revenue (estimated) €60,675 IT infrastructure Bing, Yahoo, and Google The technology used by Lilo incorporates the algorithms of major search engines such as Bing, Yahoo, or Google. When a user performs the search, Lilo relies on the algorithms of the search engines to show the results and, simultaneously with the display of the information searched for, commercial announcements are displayed. The last step consists of sending money through drops of water to the chosen projects. When a user uses browsers from Chrome, Firefox, and Safari, Lilo requests “do not track” to avoid unwanted tracking of users. Lilo protects privacy by blocking advertising tracking by advertisers who offer their services in the search engine; in addition, Lilo neither collects personal data, nor sells them to third parties. For the analysis of the information, Lilo does not use Google Analytics, but a proprietary tool called Piwik. This tool ensures that users’ IPs remain anonymous. The concerns of users about privacy are highlighted in Rieder research [ 27 ]. Rieder proposed the concept of “Symmetry of confidence” on the Internet. It reflects the symmetry of users’ interest in using search engines, although they know that their personal data may be at risk, users are still using search engines because it is the easiest way to find information on the Internet. This “Symmetry of confidence” can make them modify their attitude and behavior to adopt a search engine. Lilo collected money comes from the commercial links displayed during the searches carried out by the users. The income is distributed as follows: 50% goes to projects through water drops, 25% finances the activity of operation of the search engine, 20% is used to communicate the project, and the remaining 5% is used to offset carbon emissions (see Figure 1). Symmetry 2018, 10, x FOR PEER REVIEW 6 of 21 Figure 1. Distribution of income obtained by Lilo.org. Source: Lilo.org. With respect to donations, each search results in a drop of water. Users can decide which project to donate their water drops to. All projects are characterized by being innovative and having a social or environmental character. The partners propose the projects to which the contributions of other partners can be allocated through drops of water. Table 5 shows the projects that have received the most drops of water from the Lilo user community. Table 5. Projects with the most donations through drops of water (Lilo). Name of the Project Description Waterdrop Donations The Oasis project of Colibris Building an environmentally friendly society 44.467€ Arutam Zero Deforestation Combating deforestation and global warming 22.346€ Agrisud International Creating very small-scale sustainable farms 14.669€ Ecological cookers Stock-pilling CO 2 by encouraging the use of solar cookers 3.514€ GreenWave Saving seas 1513€ Mazí Mas Supporting women from migrant and refugee communities 840€ Source: Lilo.org. 2.3. Behavioral Intention to Adopt a Meta Search Engine that Favors Sustainable Water Management Recent years have witnessed the development of numerous new technologies, resulting in the launch of many new products on the market [28]. However, of these new technological products, some have worked, but 90% have failed [29]. Although a social search engine may appear to be a good idea, since it is supported by big trends in consumer behavior such as the growing number of search engines, the increasing number of searches made daily by users and the concern that people show for the environment, it is necessary to understand if those users have behavioral intentions with respect to the adoption of this search engine and identify the key factors determining the success or failure of a search engine. To this end, in the present study, we have reviewed the most important works that address the behavioral intention of users with respect to new technologies—in particular, those related to the Internet, mobile devices and search engines on-line (Table 6). In his work, Hsiu-Fen [30] analyzed user intentions to be part of online communities. This study was based on the TPB in its extended model and aimed to identify the key factors that will make users part of these online communities. Some of the variables taken into account were the perceived utility, ease of use, facilitating conditions, and trust. The analysis is done with SEM and AMOS. Figure 1. Distribution of income obtained by Lilo.org. Source: Lilo.org. Symmetry 2018,10, 584 6 of 21 With respect to donations, each search results in a drop of water. Users can decide which project to donate their water drops to. All projects are characterized by being innovative and having a social or environmental character. The partners propose the projects to which the contributions of other partners can be allocated through drops of water. Table 5shows the projects that have received the most drops of water from the Lilo user community. Table 5. Projects with the most donations through drops of water (Lilo). Name of the Project Description Waterdrop Donations The Oasis project of Colibris Building an environmentally friendly society 44.467€ Arutam Zero Deforestation Combating deforestation and global warming 22.346€ Agrisud International Creating very small-scale sustainable farms 14.669€ Ecological cookers Stock-pilling CO2by encouraging the use of solar cookers 3.514€ GreenWave Saving seas 1513€ MazíMas Supporting women from migrant and refugee communities 840€ Source: Lilo.org. 2.3. Behavioral Intention to Adopt a Meta Search Engine that Favors Sustainable Water Management Recent years have witnessed the development of numerous new technologies, resulting in the launch of many new products on the market [ 28 ]. However, of these new technological products, some have worked, but 90% have failed [29]. Although a social search engine may appear to be a good idea, since it is supported by big trends in consumer behavior such as the growing number of search engines, the increasing number of searches made daily by users and the concern that people show for the environment, it is necessary to understand if those users have behavioral intentions with respect to the adoption of this search engine and identify the key factors determining the success or failure of a search engine. To this end, in the present study, we have reviewed the most important works that address the behavioral intention of users with respect to new technologies—in particular, those related to the Internet, mobile devices and search engines on-line (Table 6). In his work, Hsiu-Fen [ 30 ] analyzed user intentions to be part of online communities. This study was based on the TPB in its extended model and aimed to identify the key factors that will make users part of these online communities. Some of the variables taken into account were the perceived utility, ease of use, facilitating conditions, and trust. The analysis is done with SEM and AMOS. Table 6. Previous studies: A review. Author Description Aim Object Hsiu-Fen [30] Explaining the behavioral intention by identifying the key factors that favor users’ participation in virtual communities. The model is based on the Theory of Planned Behavior (TPB) and includes variables such as perceived utility, ease of use, facilitating conditions, trust. The analysis is done with SEM (Structural Equation Modeling). Behavioral intention Virtual communities Lu, Yao, & Yu [31] Studying the keys factors determine the adoption of mobile phones as devices to connect to the Internet and search it. The analysis is done with SEM and the AMOS (Analysis of Moment Structures) as a statistical model in which constructs such as Social Influence, Perceived ease of use, Perceived usefulness or Perceived innovativeness are included. Behavioral intention Wireless Internet services Symmetry 2018,10, 584 7 of 21 Table 6. Cont. Author Description Aim Object Sung, Jeong, Jeong & Shin [32] Identifying the factors that determine the intention to adopt mobile technologies for learning. The analysis is carried out with modeling with structural equations (SEM) using the AMOS. Among the constructs are the social influence, the expectation of effort, and self-efficacy. The results show that those responsible for mobile learning had to focus on user self-efficacy to improve their behavioral intention. Behavioral intention Mobile devices for e-learning Morgan-Thomas & Veloutsou [33] Developing an on-line brand adoption model that integrates the Information Systems and marketing approach. SEM is used for the analysis. Among the studied factors are trust, perceived usefulness, and behavioral intention. Technology acceptance/ Behavioral intention On-line brands Tai [34] Analyzing factors such as habit, hedonic motivations, and utilitarian motivations that influence the behavioral intentions of users vis-à-vis search engines. The AMOS is used for data analysis. Behavioral intention Meta search engines Furthermore, Francis et al. [ 35 ], Mathieson [ 36 ] and Ajzen [ 37 ] also used the TPB as a starting point to better understand the factors that affect user behavioral intention in Information Technologies. Finally, Sung, Jeong, Jeong & Shin [ 32 ], Morgan-Thomas & Veloutsou [ 33 ], and Lu, Yao, and Yu [ 31 ] used SEM and AMOS to identify the behavioral intention of mobile device users. Likewise, Tai [ 34 ] focused on identifying the key behavioral variables of online searchers. Among the factors analyzed in the latter study were habit, hedonic motivations, and utilitarian motivations. Structural equations and the AMOS were used to perform the analysis and hypothesis testing. 3. Hypothesis Development and Research Model Following Hsiu-Fen [ 30 ], Mathieson [ 36 ], and Ajzen [ 37 ], in the present study, we have taken the Theory of Planned Behavior as a starting point to develop our model and formulate the hypotheses. The Theory of Planned Behavior has received much scholarly attention in the domain of Information Technologies (IT) [ 37 , 38 ]. All this body of work has highlighted the value of this theory, characterized by multidimensionality of its components, in terms of describing and predicting user behavior in digital environments [ 39 , 40 ]. The Theory of Planned Behavior is used in this research due to the fact that search engines have given rise to concepts such as “Symmetry of confidence”, in which Reference [ 27 ] links data privacy on the Internet with the decisions taken by users with respect to the adoption of a search engine. Likewise, studies like Reference [ 41 ] also worked on the mathematical concept of symmetry in order to link a ranking relational search results list with the behavior of users when they use a search engine. In this research, facilitating conditions implies that the technical conditions of the user or the environment are sufficient to support innovations or the adoption of a new technology like search engines that supports data privacy or the “Symmetry of confidence” and sustainable purposes. According to Venkatesh et al. [ 42 ], facilitating conditions affect both behavioral intention and use. Based on the above, the following prediction can be formulated: Hypothesis 1 (H1). Facilitating conditions would have a positive effect on user behavioral intention. Behavioral intention refers to the purpose of using a particular technology over time [ 37 ]. There have been several studies on how social influence impacts this behavior in the long term [ 43 – 45 ]. Other studies, such as Alaiad and Zhou [ 46 ], found that social influence was the most prominent predictor of user behavioral intention. Therefore, we expect the following: Hypothesis 2 (H2). Social influence would have a positive effect on user behavioral intention. Symmetry 2018,10, 584 8 of 21 On the other hand, the facilitating conditions—i.e., elements that allow a user to adopt a technology so that the greater the facilities available to the user, the less effort should be devoted to adopt this new technology—are an important element in the TPB, since they influence both user behavior and intention [ 37 ]. In this context, it can be predicted that the search engines can improve the sustainable water management [ 30 , 42 ]. Also, the more prepared the user is to use a certain technology, such as online search engines, the easier it will be to generate the habit of using that technology [27,47,48]. Based on the above, the following predictions can be formulated: Hypothesis 3 (H3). Facilitating conditions would have a positive effect on effort expectancy. Hypothesis 4 (H4). Facilitating conditions would have a positive effect on habits. Social influence is the measure in which users understand that people in their environment have an influence on their decision making. Users might think that this environment wants them to use a technology, mobile device or an online search engine [49,50]. The influence of the social environment of the user occurs in various constructs [ 31 ]. On the one hand, there are habits. According to Sánchez [ 51 ], both the family and school environment of the children will condition the habits they develop. An example would be reading or the use of technologies. It is exactly the new information and communication technologies that are setting the pace of the users, and it is the families, the environment, and the social influences received from the outside that can help children generate healthy habits [ 52 ]. Based on the above, we can predict the following: Hypothesis 5 (H5). Social influence would have a positive effect on habits. Regarding the impact of social influence on trust, in a new technology, trust must be won over time and experience [ 53 ]. However, due to social influence, the time normally required to develop confidence in a new technology may be shortened [49]. Next, according to Durkheim [ 54 ] and Santos [ 55 ], happiness, fun, and hedonic motivation increase with an increase of the number of people in users’ immediate environment and with the development of closer relationships between users and people in immediate environment. The same holds true regarding the adoption of new technologies, such as a search engine that supports the efficient management of water. That is, social influence can be expected to have an effect on user happiness and hedonic motivation [34]. Based on the above, the following predictions can be formulated: Hypothesis 6 (H6). Social influence would have a positive effect on trust. Hypothesis 7 (H7). Social influence would have a positive effect on hedonic motivation. According to Venkatesh et al. [ 42 ], effort expectancy can be understood as the degree of ease related to the use of a certain technology. The more successful the effort that the user believes s/he should devote to adopting a technology, the greater the hedonic motivation that will be produced [ 44 , 45 ] and the greater the confidence that will be generated in that determined technology—in our case, a search engine [34]. Accordingly, we predict the following: Hypothesis 8 (H8). Effort expectancy would have a positive effect on Hedonic motivation. Hypothesis 9 (H9). Effort expectancy would have a positive effect on Trust. The habit reflects the tendency to repeat a behavior that occurred in the past when the same circumstances occur again in the future [ 56 ]. Habit is guided by automatic cognitive processes, rather Symmetry 2018,10, 584 9 of 21 than by elaborate decision processes [ 57 ]. Mcknight, Choudhury, and Kacmar, [ 58 ] related the habits generated in the consumers of online stores with the trust, highlighting that this trust is not only in the first purchase, but also that it extends over time for repeated purchases. Therefore, the following prediction can be formulated: Hypothesis 10 (H10). Habits would have a positive effect on trust. In digital environments, i.e., where there is no direct contact between the provider of an on-line search service and the user, trust becomes a determining factor for the adoption of technologies [ 59 – 61 ]. Furthermore, the adoption of these products or technologies produces a psychological and emotional satisfaction in the users, coupled with the sense of entertainment, which is jointly known as hedonic motivation [ 62 ]. Gefen et al. [ 63 ] states that trust has an impact on perceived enjoyment and on hedonic motivation. Accordingly, we expect the following to be true: Hypothesis 11 (H11). Trust would have a positive effect on hedonic motivation. Among the factors that influence behavioral intention is Effort expectancy [ 64 ]. The decision to adopt a new technology—in this case, a search engine for sustainable purposes—will be determined by the effort that users believe they should devote to learning and adopting this new technology [34,65]. According, our prediction is as follows: Hypothesis 12 (H12). Effort expectancy would have a positive effect on behavioral intention. In addition, considering that having a habit implies that we continue to do the same thing for a long time without considering other options [ 47 , 48 ], habits can influence behavior. In this respect, Ling Tai [ 34 ] analyzed how habits influence the behavioral intention of online search users. Accordingly, following Ling Tai [ 34 ] who analyzed how habits influence behavioral intention of online search users, we predict the following: Hypothesis 13 (H13). Habits would have a positive effect on behavioral intention. Another factor within the framework of the Theory of Planned Behavior is trust [ 30 , 66 ]. While trust is also a barrier to electronic transactions [ 67 ], if a trust relationship is created, users will develop behavioral intention [ 68 ]. For instance, this relationship between trust and behavioral intention was demonstrated by Morgan-Thomas [33]. Based on the above, we expect the following: Hypothesis 14 (H14). Trust would have a positive effect on behavioral intention. In their work on online search engine adoption by users, Ling Tai [ 34 ] identified habit and hedonic motivations as two major factors that influence behavioral intention. Furthermore, Venkatesh, Thong, and Xu [ 50 ] analyzed the relationship between hedonic motivation and behavioral intention and found that, if the activity developed produces happiness to the user, this user will develop behavioral intention. Based on the above, our last hypothesis is as follows: Hypothesis 15 (H15). Hedonic motivation would have a positive effect on behavioral intention. Based on the 15 hypotheses formulated above, the research model has been constructed (see Figure 2). Symmetry 2018,10, 584 16 of 21 Moreover, the results show that users are happy about their use of a search engine that contributes to a more fair and equitable distribution of water resources. This result confirms previous studies [ 34 , 50 ] and demonstrates the influence of this emotion of happiness on the behavioral intention. The effect of social influence on habits ( β = 0.130, t = 2.931), trust ( β = 0.213, t = 4.429), and hedonic motivation ( β = 0.105, t = 2.437) increases, suggesting that the users of Lilo, in their use of this search engine, experience more happiness and confidence, as well as have a tendency to rely on habit. Finally, the effort expectancy construct was found to have a positive influence on hedonic motivation ( β = 0.140, t = 2.919), trust ( β = 0.158, t = 2.490), and behavioral intention ( β = 0.160, t = 2.878). The final explanatory capacity of effort expectancy was moderate and close to weak, similarly to that of behavioral intention (R2= 55.8%). Taking into account the results obtained and collected in Figure 3, as well as the discussion made in this section, by way of summary in Table 11 the hypotheses and the results obtained are presented. Symmetry 2018, 10, x FOR PEER REVIEW 16 of 21 Therefore, it appears that, in the educational environment, the use of this type of search engine is strongly influenced by habit, which suggests that users do not consider using another search engine. Moreover, the results show that users are happy about their use of a search engine that contributes to a more fair and equitable distribution of water resources. This result confirms previous studies [34,50] and demonstrates the influence of this emotion of happiness on the behavioral intention. The effect of social influence on habits (β = 0.130, t = 2.931), trust (β = 0.213, t = 4.429), and hedonic motivation (β = 0.105, t = 2.437) increases, suggesting that the users of Lilo, in their use of this search engine, experience more happiness and confidence, as well as have a tendency to rely on habit. Finally, the effort expectancy construct was found to have a positive influence on hedonic motivation (β = 0.140, t = 2.919), trust (β = 0.158, t = 2.490), and behavioral intention (β = 0.160, t = 2.878). The final explanatory capacity of effort expectancy was moderate and close to weak, similarly to that of behavioral intention (R 2 = 55.8%). Taking into account the results obtained and collected in Figure 3, as well as the discussion made in this section, by way of summary in Table 11 the hypotheses and the results obtained are presented. Figure 3. Result of Structural Equation Model. * p < 0.05; ** p < 0.01; *** p < 0.001; n.s. insignificant at the 0.05 level. χ 2 (df) = 576.844 (p = 0.000), GFI = 0.904, AGFI = 0.880, CFI = 0.946, RMSEA = 0.056, χ 2 Normalizada(χ 2 /df) = 2.404. Figure 3. Result of Structural Equation Model. * p< 0.05; ** p< 0.01; *** p< 0.001; n.s. insignificant at the 0.05 level. χ2 (df) = 576.844 (p= 0.000), GFI = 0.904, AGFI = 0.880, CFI = 0.946, RMSEA = 0.056, χ2Normalizada (χ2/df) = 2.404. Table 11. Summary of the results on hypotheses testing. Hypothesis Relations Result H1 Facility Condition →Behavioral Intention Not supported H2 Social Influence →Behavioral Intention Not supported H3 Facility Condition →Effort Expectancy Supported H4 Facility Condition →Habits Supported H5 Social Influence →Habits Supported H6 Social Influence →Trust Supported H7 Social Influence →Hedonic Motivation Supported H8 Effort Expectancy →Hedonic Motivation Supported H9 Effort Expectancy →Trust Supported H10 Habits →Trust Supported H11 Trust →Hedonic Supported H12 Effort Expectancy →Behavioral Intention Supported H13 Habits →Behavioral Intention Supported H14 Trust →Behavioral Intention Not supported H15 Hedonic Motivation →Behavioral Intention Supported Symmetry 2018,10, 584 17 of 21 7. Implications and Conclusions Given that the sustainable management of water resources involves different actors, it is necessary to take into account their points of view and their interests so that the adoption of measures is effective, as in this case the use of a social search engine that supports the projects related to water and sustainable projects that can help sustain responsible policies [92,93]. Since, according to recent estimates, by 2025, around two-thirds of the population will experience water shortages, it is necessary to look for alternatives. One option is to support those initiatives that present proven solutions to improve sustainable management of water resources based on new social and technological developments. For example, the introduction of social search engines that improve the management of water resources and promote social projects that actively support a more effective water management. The number of queries made in search engines is growing every day. Therefore, considering that, by 2030, each inhabitant of the planet is expected to have on average 6 devices connected to the Internet, it is important to consider the externalities generated by the activity of those search engines and compensate for these adverse effects by concrete actions for the efficient management of water around the world. The innovation driven by the development of new technologies has led to the emergence of new sustainable business models that can help improve the water management models of today. The sustainable search engine presented in the present study proposes the improvement of water management achieved through its own use. The results obtained in the present study demonstrate that an Internet search engine that contributes to the effective management of water makes individuals who intend to use it develop hedonic motivations and, therefore, feel happy because they think that they are developing a responsible and sustainable approach to water management. Similarly, the success of this technological product is closely related to the ability it grants to its users to exert a social impact on the environment, rather than to its ability to gain users’ trust in what they do or in their results. This is so mainly because the search engine model is beneficial to society and its sustainable development, as well as to the maintenance of the natural resources. However, habit will definitely be the main factor determining the improvement of the product, since, as our results demonstrate, habit has both direct and indirect influences on behavioral intention. The developers and designers of this and similar products should take into consideration that users have low expectations of effort since, otherwise, they will not show the behavioral intention to use this type of product. A limitation of the present study is that our sample of respondents, who were users of the Lilo search engine, was limited to professionals and postgraduate students. Therefore, future research should also focus on contexts other than education and be conducted in countries where effective management of water resources is of vital importance for the country itself. Author Contributions: All authors contributed equally to this work. All authors wrote, reviewed, and commented on the manuscript. All authors have read and approved the final version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. References 1. Saura, J.R.; Palos-Sánchez, P.; CerdáSuárez, L.M. 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