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This study aims to provide a comprehensive analysis of the current privacy concerns in the tourism industry by uncovering the key factors leading to such concerns (i.e. smart public services, cyber security issues, consumer behaviour and governance). Using papers from multiple sources, the relationship between technology advancements and tourist’s privacy concerns has been established.

This study adopted a machine learning-based systematic literature review approach to find out the clusters. The study analysed 68 papers using the topic modelling approach. A four-cluster solution was considered to be most representative of the extant literature identified using bibliographic coupling. Finally, content analysis of the selected literature has been performed.

This study identified four factors majorly leading to privacy concerns amid increasing technological advancements. Moreover, these factors were found to have a dyadic relationship with technological advancements. To everyone’s amazement, sustainable tourism was also found to have led to privacy concerns among tourists along with a lack of governance and cyber security issues. Furthermore, cluster-wise future research directions are provided based on the content analysis.

This study contributes to the literature by systematically reviewing and identifying the four dimensions leading to privacy concerns. To the best of the authors’ knowledge, the study done is the only attempt to synthesize the extant literature on tourists’ privacy concerns using an unbiased scientific approach.

本研究对旅游业当前的隐私问题进行了全面分析, 以揭示导致此类担忧的关键因素（即智能公共服务、网络安全问题、消费者行为和治理）。 通过考察来自多方渠道的现有文献, 本研究确立了技术进步与游客隐私问题之间的关系。

本研究采用基于机器学习的系统文献综述方法来找出聚类。 该研究使用主题建模方法分析了 68 篇文章。四聚类解决方案是现有文献中最具代表的 文献分类方法。最后, 本研究对所选文献进行了内容分析。

这项研究确定了在技术进步不断增加的情况下主要导致隐私问题的四个因素。 此外, 这些因素被发现与技术进步有二元关系。 本研究还发现可持续旅游业引发了游客对隐私的担忧以及缺乏治理和网络安全问题。 此外, 通过基于内容的分析, 本研究提供了未来的聚类研究方向。

本研究对现有文献的贡献在于通过系统地文献综述和分析以确定导致隐私问题的四个维度。该研究是基于公正的科学方法综合现有游客隐私问题文献的首度尝试。

Este estudio proporciona un análisis exhaustivo de la actual preocupación por la privacidad en el sector turístico, poniendo al descubierto los factores clave que la generan (los servicios públicos inteligentes, los problemas de ciberseguridad, el comportamiento de los consumidores y la gobernanza). Gracias al uso de artículos de múltiples fuentes, se ha establecido la relación entre los avances tecnológicos y la preocupación de los turistas por la privacidad.

Este estudio adoptó un enfoque de revisión sistemática de la literatura basado en el aprendizaje automático para descubrir los conglomerados. El estudio analizó sesenta y ocho artículos utilizando el enfoque de modelización de temas. Se consideró que una solución de cuatro conglomerados era la más representativa de la literatura existente identificada mediante el acoplamiento bibliográfico. Por último, se realizó un análisis de contenido de la bibliografía seleccionada.

En este estudio se identificaron cuatro factores principales que suscitan inquietud por la privacidad en medio de los crecientes avances tecnológicos. Además, se descubrió que estos factores tienen una relación diádica con dichos avances. Para sorpresa de todos, se halló que el turismo sostenible, junto con la falta de gobernanza y los problemas de ciberseguridad, también suscitan entre los turistas preocupaciones por su privacidad. Finalmente, el análisis de contenido ofrece orientaciones para futuras investigaciones.

Este estudio contribuye a la literatura haciendo una revisión sistemática e identificando las cuatro dimensiones que conducen a la preocupación por la privacidad. Este estudio es el único intento de sintetizar la bibliografía existente sobre la preocupación de los turistas por su privacidad utilizando un enfoque científico imparcial.

The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.

A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.

The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.

The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.

The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.

A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.

Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.

The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.

A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.

The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

This research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.

Identification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.

The findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.

This is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.

This study extends the literature on the effectiveness of e-learning by investigating the role of student engagement on perceived learning effectiveness (PLE) in the context of Indian higher education. Further, the impact of personal factors (Internet self-efficacy (ISE)) and environmental factors (information, system and service quality parameters) on various dimensions of student engagement (behavioral, emotional and cognitive) is studied through the lens of social cognitive theory (SCT).

An online management information systems (MIS) course is delivered to a batch of 412 postgraduate students. An online survey was conducted to measure the factors affecting their PLE. In addition to the survey, a summative assessment is conducted to evaluate the students in terms of their marks to assess their achievements (actual learning). Covariance-based structural equation modeling (CB-SEM) is used to validate the developed research model.

It is discovered that the IS (information system) quality parameters (environmental factors) positively impact PLE. The ISE affects the PLE through the mediating effect of all the dimensions of student engagement. Furthermore, there exists a positive relationship between PLE and student marks.

This study develops a research model using personal and environmental factors to understand PLE through the lens of SCT and then empirically validates it. The psychological process from the students' ISE to the PLE is explained through the mediating effects of various dimensions of engagement. Further, it is found that the PLE is positively related to student marks.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy.

The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool “N-Vivo.” The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques.

This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders.

Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value.

This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature.