Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Nature dependent tourism - Combining big data and local knowledge

The ability to quantify nature's value for tourism has significant implications for natural resource management and sustainable development policy. This is especially true in the Eastern Caribbean, where many countries are embracing the concept of the Blue Economy. The utilization of user-generated content (UGC) to understand tourist activities and preferences, including the use of artificial intelligence and machine learning approaches, remains at the early stages of development and application. This work describes a new effort which has modelled and mapped multiple nature dependent sectors of the tourism industry across five small island nations. It makes broad use of UGC, while acknowledging the challenges and strengthening the approach with substantive input, correction, and modification from local experts. Our approach to measuring the nature-dependency of tourism is practical and scalable, producing data, maps and statistics of sufficient detail and veracity to support sustainable resource management, marine spatial planning, and the wider promotion of the Blue Economy framework.

How to Monitor the Transition to Sustainable Food Services and Lodging Accommodation Activities: A Bibliometric Approach

The transition to sustainable food systems is one of the main challenges facing national and international action plans. It is estimated that food services and lodging accommodation activities are under pressure in terms of resource consumption and waste generation, and several tools are required to monitor their ecological transition. The present research adopts a semi-systematic and critical review of the current trends in the food service and lodging accommodation industries on a global scale and investigates the real current environmental indicators adopted internationally that can help to assess ecological transition. This research tries to answer the subsequent questions: (i) how has the ecological transition in the food service industry been monitored? and (ii) how has the ecological transition in the lodging accommodation industry been monitored? Our study reviews 66 peer-reviewed articles and conference proceedings included in Web of Science between 2015 and 2021. The results were analyzed according to content analysis and co-word analysis. Additionally, we provide a multidimensional measurement dashboard of empirical and theoretical indicators and distinguish between air, water, energy, waste, health, and economic scopes. In light of the co-word analysis, five research clusters were identified in the literature: “food cluster”, “water cluster”, “consumers cluster”, “corporate cluster”, and “energy cluster”. Overall, it emerges that food, water, and energy are the most impacted natural resources in tourism, and users and managers are the stakeholders who must be involved in active monitoring.

Bringing Smart Home Technology to Peer-to-Peer Accommodation: Exploring the Drivers of Intention to Stay in Smart Accommodation

COVID-19 has caused disruptions in the sharing economy for both platforms and owners, who are typically micro-businesses. Lower demand and ample supply means that users have a great deal of choice. Finding ways for properties to differentiate themselves has been a pressing need. Against this background, this paper pursued two objectives: firstly to explore the perceived functional and emotional value of smart accommodation and the factors contributing to this by adopting the Theory of Consumption Values, and secondly to examine the role of perceived value in driving intention to stay in smart accommodation in the future. 430 responses were collected to analyse the relationships among antecedents, value and intention. The results showed that the functional value of smart accommodation is associated with the perception that such accommodation represents good value for the price, smart devices are useful, they can enhance control of stay experiences, and there are resources and opportunities facilitating the use of technology. Emotional value is determined by the perception that staying in smart accommodation represents sustainable behaviour, the integration of smart home technologies offers control over the stay experience, improves the entertainment experience, aesthetics and playfulness of using technology. Emotional values are inhibited by the perception of surveillance in smart accommodation. Also, the study offers evidence of the correlation of intention with functional and emotional value. The evidence contributes to the literature by explaining the potential implications of innovative technologies for business recovery in the post-pandemic reality, exploring the applications of smart technologies in delivering tourism services, and identifying the factors in the adoption of smart homes in the hospitality sector. The findings provide practical implications for facilitating the applications of innovative technology and its adoption in home and non-home environments.

Environmental Sustainability Strategies for Smaller Companies in the Hotel Industry: Doing the Right Thing or Doing Things Right?

The connection between tourism and nature justifies the environmental concerns from tourism agents, namely global hotel chains. This paper explores the differences between smaller hotel chains and their larger global counterparts regarding environmentally sustainable practices. The research approach is qualitative, based on the analysis of 40 company websites and in-depth interviews with 18 entrepreneurs and executives. Results suggest that environmental issues are, for most companies, not a response to societal challenges (‘doing the right thing’), but a response to owners’ concerns (‘doing things right’). Hotel chains develop environmental sustainability practices, mainly for cost-reduction purposes, accommodating the owners’ demands for efficiency. Notwithstanding, there are differences according to the chain’s size. Smaller companies are less prone to adopt environmental practices and to invest in communicating them than global chains. Concerning sustainability in the hotel industry, most studies focus on specific topics and discussions. A more holistic approach to sustainability to establish a deeper understanding of sustainable business decisions in the hotel sector is scarce in the literature. This paper addresses this gap by exploring the strategic reasons behind the sustainable practices of hotel companies, namely smaller ones. Managerial implications of the results are also derived in this paper.

Research on the Role of Influencing Factors on Hotel Customer Satisfaction Based on BP Neural Network and Text Mining

With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels.

Sustainable HRM through Improving the Measurement of Employee Work Engagement: Third-Person Rating Method

The purpose of the paper is to present the survey findings of two alternative methods (self-rating (SR) and third-person rating (TPR)) of measuring employee work engagement (EWE). The potential impacts of gender, job tenure, position, and work condition on TPR vs. SR were also investigated. A sample of 649 of hotel service workers, supervisors, and managers in China participated in the study. An accurate measure of employee work engagement serves as a leading indicator of turnover intention and an early diagnostic tool for sustainable human resource management. Despite its popularity as a work engagement measure, SR method has many limitations. This research attempted to demonstrate that TPR is a viable and better alternative measure of EWE. The results indicated that TPR does possess desirable measurement characteristics, such as convergent validity, nomological validity, and structure invariant. TPR also provides a more conservative, and perhaps more accurate as well, measure of EWE. The difference in mean EWE scores as measured by SR vs. TPR was found to be affected by the specific dimension under study, with the least observable absorption dimension the most affected. The difference was also found to be significantly higher for males than for females, bigger as an employee’s position moves higher, and larger as the length of job tenure increases. Additionally, the difference in satisfaction–EWE correlations, as measured by TPR vs. SR, were much higher when the work conditions were poor. For practitioners, the importance of this study lies in the fact that TPR, as a conservative measure of EWE, can play an important role in detecting early signs of employee troubles sooner and lead management to take timely actions, making human resource management more sustainable. For academics, the results that SR and TPR of EWE generally result in similar pattern of findings offer strong encouragement to build future research on EWE through the TPR method.

The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

The objective of this work is to detect the variables that allow organizations to manage their social network services efficiently. The study, applying machine learning algorithms and multiple linear regressions, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The authors examine (I) the characteristics of the content (publication volumes, publication components, and publication moments) and (II) the message of the content (publication topics). The research considers 21,771 publications and thirty-nine variables. The results show that the recognition obtained through retweets and favorites is conditioned both by the characteristics of the content and by the message of the content. The recognition through retweets improves when the organization uses links, hashtags, and topics related to gender equality, whereas the recognition through favorites increases when the organization uses original tweets, publications between 8:00 and 10:00 a.m. and, again, gender equality related topics. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers’ purchase intention and behavior. Therefore, it is important to understand the customer’s experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as “Intangible Service”, “Physical Environment”, “Purpose”, and “Location”. The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are “Access”, “F&B (Food and Beverage)”, “Purpose”, “Tangibles”, and “Empathy”. Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested.

Investigating Sustainable Practices in Hotel Industry-from Employees’ Perspective: Evidence from a Mediterranean Island

Although several studies have researched the hotel employees’ environmental behavior, none has addressed the hotel employees’ perception of their respective hotels’ sustainability practices. This study aims to investigate the sustainable practices in four and five star hotels in a Mediterranean island by employing Global Sustainable Tourism Council (GSTC) hotel criteria indicators, indicators of sustainable development for tourism destinations (WTO), and the European Union’s (EU) sustainability framework for the Mediterranean hotels− “Nearly Zero-Energy Hotels” (NEZEH), and global sustainable development goals (SDG) in the context of three dimensions: social, economic and environment. The sampled hotels claim that their operation system is conformed to sustainability principles with the aim of furthering their green agenda. In this study, we aim to investigate the validity and extent of this claim. About 290 (N = 290) employees in the specified hotels were surveyed. The measurement instruments were compiled based on sustainability indicators that encompassed addressing social, economic, and environmental dimensions. The research questions contextualized around four main themes: effective sustainability planning, maximizing social and economic benefits for the local community, enhancing cultural heritage, and reducing negative environmental impacts. For the statistical and data analysis, SEM (structural equation modeling) is used. Study revealed that employees are a legitimate and credible source of information about sustainability practices. It is also revealed that as going green is becoming a means toward branding, hotels are making efforts to implement a genuine sustainability practice. Study also indicated that the majority of employees validated the sustainability practices as genuine.

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review

In the last several decades, electronic word of mouth (eWOM) has been widely used by consumers on different digital platforms to gather feedback about products and services from previous customer behavior. However, this useful information is getting blurred by fake reviews-i.e., reviews that were created artificially and are thus not representative of real customer opinions. The present study aims to thoroughly investigate the phenomenon of fake online reviews in the tourism sector on social networking and online reviews sites. To this end, we conducted a systematic review of the literature on fake reviews for tourism businesses. Our focus was on previous studies that addressed the following two main topics: (i) tourism (ii) fake reviews. Scientific databases were used to collect relevant literature. The search terms "tourism" and "fake reviews" were applied. The database of Web of Science produced a total of 124 articles and, after the application of different filters following the PRISMA 2009 Flow diagram, the process resulted in the selection of 17 studies. Our results demonstrate that (i) the analysis of fake reviews is interdisciplinary, ranging from Computer Science to Business and Management, (ii) the methods are based on algorithms and sentiment analysis, while other methodologies are rarely used; and (iii) the current and future state of fraudulent detection is based on emotional approaches, semantic analysis and new technologies such as Blockchain. This study also provides helpful strategies to counteract the ubiquity of fake reviews for tourism businesses.

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

Cultural Heritage Appraisal by Visitors to Global Cities: The Use of Social Media and Urban Analytics in Urban Buzz Research

An attractive cultural heritage is an important magnet for visitors to many cities nowadays. The present paper aims to trace the constituents of the destination attractiveness of 40 global cities from the perspective of historical-cultural amenities, based on a merger of extensive systematic databases on these cities. The concept of cultural heritage buzz is introduced to highlight: (i) the importance of a varied collection of urban cultural amenities; (ii) the influence of urban cultural magnetism on foreign visitors, residents and artists; and (iii) the appreciation for a large set of local historical-cultural amenities by travelers collected from a systematic big data set (emerging from the global TripAdvisor platform). A multivariate and econometric analysis is undertaken to validate and test the quantitative picture of the above conceptual framework, with a view to assess the significance of historical-cultural assets and socio-cultural diversity in large urban agglomerations in the world as attraction factors for visitors. The results confirm our proposition on the significance of urban cultural heritage as a gravity factor for destination choices in international tourism in relation to a high appreciation for historical-cultural amenities.

A Text Mining Approach for Sustainable Performance in the Film Industry

Many previous studies have shown that the volume or valence of electronic word of mouth (eWOM) has a sustainable and significant impact on box office performance. Traditional studies used quantitative data, such as ratings, to measure eWOM. However, recent studies analyzed unstructured data, such as comments, through web-based text analysis. Based on recent research trends, we analyzed not only quantitative data, like ratings, but also text data, like reviews, and we performed a sentiment analysis using a text mining technique. Studies have also examined the effect of cultural differences on the decision-making processes of individuals and organizations. We applied Hofstede’s cultural theory to eWOM and analyzed the moderating effect of cultural differences on eWOM influence. We selected 338 films released between 2006 and 2015 from the BoxOfficeMojo database. We collected ratings and reviews, box office revenues, and other basic information from the Internet Movie Database (IMDb). We also analyzed the effects of cultural differences, such as power distance, individualism, uncertainty avoidance, and masculinity, on box office performance. We found that user comments have a greater impact on film sales than user ratings, and movie stars and co-production contribute to box office success. We also conclude that cultural and geographical differences moderate the sentiment elasticity of eWOM.

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors.

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

Exploring How Homophily and Accessibility Can Facilitate Polarization in Social Networks

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants on topics surrounding politics, climate, the economy and other areas where an agreement is required. This work investigates into greater depth a type of model that can produce ideological segregation as a result of polarization depending on the strength of homophily and the ability of users to access similar minded individuals. Whether increased access can induce larger amounts of societal separation is important to investigate, and this work sheds further insight into the phenomenon. Center to the hypothesis of homophilic alignments in friendship generation is that of a discussion group or community. These are modeled and the investigation into their effect on the dynamics of polarization is presented. The social implications demonstrate that initial phases of an ideological exchange can result in increased polarization, although a consensus in the long run is expected and that the separation between groups is amplified when groups are constructed with ideological homophilic preferences.

Key Factors of Sustainability for Smartphones Based on Taiwanese Consumers’ Perceived Values

The rapid growth of smartphones over recent decades has brought a large amount of e-waste as well as an increased carbon footprint. Facing severe environmental issues, sustainable development of smartphones has become a particularly important public concern. The main aim of this study was to clarify the key factor of sustainability for smartphones based on Taiwanese consumers’ perceived values. Apple’s iPhone was taken as an example. First, key factors of perception that smartphone consumers valued the most in terms of sustainable practice were extracted through a factor analysis. Second, demographic differences related to these key factors were investigated through t-test and one-way ANOVA analyses; demographic variables were gender, age, education level, occupation, and income level. The results were as follows: (1) the key factors were “recognition”, “brand advantage”, “service quality”, “usage period”, and “perceived price”; (2) there was a significant difference between genders on the key factors of perceived value (“recognition”, “brand advantage”, and “perceived price”). Specifically, females have higher perceived values of “recognition”, “brand advantage”, and “perceived price” than males; (3) there was a significant effect of income level on the key factor (“perceived price”) of perceived value. Specifically, respondents with an income level of NTD15,001–30,000 had a higher perceived value of “perceived price” than respondents earning NTD30,001–45,000. Among the five key factors, “recognition” and “brand advantage” are primary factors influencing purchase motivation; “recognition”, “brand advantage”, and “service quality” are primary factors that could influence brand loyalty; “perceived price” is the primary factor that affects purchase intention. This study contributes to the green market segmentation of smartphones. The limitations of the study relate to the size and distribution of the samples.

Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach

The main objective of this exploratory study is to identify the social, economic, environmental and cultural factors related to the sustainable care of both environment and public health that most concern Twitter users. With 336 million active users as of 2018, Twitter is a social network that is increasingly used in research to get information and to understand public opinion as exemplified by Twitter users. In order to identify the factors related to the sustainable care of environment and public health, we have downloaded n = 5873 tweets that used the hashtag #WorldEnvironmentDay on the respective day. As the next step, sentiment analysis with an algorithm developed in Python and trained with data mining was applied to the sample of tweets to group them according to the expressed feelings. Thereafter, a textual analysis was used to group the tweets according to the Sustainable Development Goals (SDGs), identifying the key factors about environment and public health that most concern Twitter users. To this end, we used the qualitative analysis software NVivo Pro 12. The results of the analysis enabled us to establish the key factors that most concern users about the environment and public health such as climate change, global warming, extreme weather, water pollution, deforestation, climate risks, acid rain or massive industrialization. The conclusions of the present study can be useful to companies and institutions that have initiatives related to the environment and they also facilitate decision-making regarding the environment in non-profit organizations. Our findings will also serve the United Nations that will thoroughly review the 17 SDGs at the High-level Political Forum in 2019.

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

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.

The Economic Potential to Support Sustainability through Household Consumption Choices

The amount of money that could potentially be saved by households by reducing unnecessary consumption and directed to sustainable investments without compromising the social needs in Finnish households was studied. The study was conducted by using statistical data and by creating short- and long-term scenarios to assess potential savings resulting from changes in household behaviour. According to the results, a Finnish household could save and subsequently allocate an average of €3400–€15,000 annually to invest in sustainability. The greatest potential for preventing unnecessary consumption is related to (1) food and drinks, and (2) transportation. In the long-term scenario, reducing expenditures in the category of housing also provides opportunities for high savings. A significant share of the saving created by sustainable patterns of consumption can be directed for example to investments in renewable energy.

Exploring Users’ Self-Disclosure Intention on Social Networking Applying Novel Soft Computing Theories

In recent years, users have increasingly focused on the privacy of social networking sites (SNS); users have reduced their self-disclosure intention. To attract users, SNS rely on active platforms that collect accurate user information, even though that information is supposed to be private. SNS marketers must understand the key elements for sustainable operation. This study aims to understand the influence of motivation (extrinsic and intrinsic) and self-disclosure on SNS through soft computing theories. First, based on a survey of 1108 users of SNS, this study used a dominance-based rough set approach to determine decision rules for self-disclosure intention on SNS. In addition, based on 11 social networking industry experts’ perspectives, this study validated the influence between the motivation attributes by using Decision-Making Trial and Evaluation Laboratory (DEMATEL). In this paper, the decision rules of users’ self-disclosure preference are presented, and the influences between motivation attributes are graphically depicted as a flow network graph. These findings can assist in addressing real-world decision problems, and can aid SNS marketers in anticipating, evaluating, and acting in accord with the self-disclosure motivations of SNS users. In this paper, practical and research implications are offered.