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There has recently been much interest in analysing the creation of personalised tourism services and studying their effect on organisations. However, there still needs to be more work in analysing their effect on happiness and the role that emotions play in these processes. This paper aims to analyse, in the context of personalised and innovative tourism services, which factors can encourage and improve managers' happiness.

A model of analysis is presented with five variables: tourism managers' happiness (TMH), innovative personalised tourism services, internal factors: emotions (IFE), organisational factors (OF) and personal factors (PF). Eight hypotheses are proposed and tested with a structural equation model.

The results allow the authors to affirm that personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also in generating emotions that contribute to improving their attitude towards the company.

This scientific work has some limitations. Firstly, this study was carried out exclusively in Spain due to the relevance of this country in the international tourism sector, according to the World Tourism Organisation. The results achieved in this research should be contrasted with other studies in other territories. Secondly, the interviews and surveys were carried out at specific time intervals. It has not led to problems of significant bias in the variance of the standard method. Therefore, it is desirable to undertake longitudinal or cross-sectional studies for future research. Thirdly, it is interesting to develop theoretical models that include other psycho-directive or leadership style constructs to determine whether they holistically enhance the subjective well-being of hospitality managers. Moreover, other types of factors of a social or strategic nature can be considered, which can positively or negatively impact the analysed variables. Finally, future research can deepen the empirical analysis of the relationship between managerial competencies and digital innovation from the perspective of happiness management. These findings would contribute to a greater cognitive understanding of the implications of personalised and innovative tourism services on hotel establishments' happiness and economic benefits.

This paper shows the fundamental role of a happy leadership style in creating responsible, green and innovative environments in today's digital society. Furthermore, the happiness of tourism managers can contribute to the generation of high quality and excellent services that are in line with the principles of sustainable development.

Personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also to generating emotions that contribute to improving their attitude towards the company. On the other hand, it has been observed that personalised and innovative tourism services generate positive effects at organisational, internal and personal levels. The following reflections are advanced: The development of internal factors such as the emotions of awe and gratitude or the generation of trust can enhance the happiness of tourism managers. The happiness of tourism business managers can be enhanced by developing OF such as smart-personalised tourism services and data protection. The happiness of tourism managers can be enhanced by the development of PF such as travellers' desires, expectations and needs, or other factors such as disposable income, health status or family situation.

This is the first empirical study that focuses on investigating how personalised and innovative tourism services affect managing happiness.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

Social networks were created to connect with friends. Therefore, communication in social networks allows addressing individual contacts of each user and is often rather private by nature. Organizations can use this to communicate frequently and personally with their stakeholders. Therefore, this study investigates how organizations use personalization in their social media communication.

This study uses a quantitative content analysis of Facebook posts from ten large companies (N = 500). The posts were analyzed regarding the sub dimensions of personalization, individualization and privatization, as well as type of post, tonality, emotions, user reactions and topics.

Companies at least partially personalize their communication on Facebook. Overall, 28% of all posts were personalized. Personalized posts were almost always positive regarding tonality and mostly positive regarding emotions. While the personalized posts in this study have fewer user reactions than non-personalized posts, they still have a rather high absolute number of reactions. Regarding personal characteristics, results show that professional competence and appearance were mainly addressed. Concerning privatization, only very few posts showed an individual in a private setting.

The present work gives an overview of how personalized posts differ from non-personalized posts and looks at depicted individuals as well as the use of private elements. By applying the conceptualization of Van Aelst et al. (2012) and by looking at organizations’ use of personalization on the social media platform Facebook, it sheds light on an area that has not been the center of attention so far and helps to expand the current state of personalization research.

The purpose of this paper is to study the profit optimization of manufacturers who provide personalized products to customers, thus avoiding additional operational costs and response times in the production process of personalized product design.

First, the authors present an integrated model for optimizing profit where the design and production of personalized products are both considered. The authors propose the concept of personalization level and four cases of personalization level are considered including top, high, medium and low levels. Polynomial-time optimal rules are provided for each level by analyzing 17 subcases where all possible personalized products are considered. Then, the authors compare the obtained profit of personalized products with the optimal profit of standardized products.

At low and high levels of personalization, personalized manufacturing is more profitable for specific products with specific characteristics. At the top and middle level of individuation, assuming that the product has certain characteristics, whether the best choice to produce personalized products depends on the level of individuation chosen by customers.

An important decision issue for manufacturers is whether to produce personalized or standardized products. In this study, the authors consider this decision problem and analyze the operational functions of personalized products. The authors hope to provide theoretical support in the operational management of manufacturers offering personalized products.

The purpose of this study is to explore the effects of personalized task communication and examine auditor willingness to rely on and factor into their judgments and decisions historically accurate internal evidence suggesting a less conservative collectability judgment and audit adjustment decision than unfavorable external and less credible internal evidence.

This study uses a 2 × 2 between-subjects experimental design manipulating whether participants receive source credibility insights leading to an evidence set consisting of mixed internal evidence signals, where historically accurate internal evidence suggests either a less conservative or similar collectability signal as unfavorable external evidence; and receive personalized or non-personalized task communication.

Through experimental analysis of 103 Big Four audit senior associates, the author found auditors factor into their collectability judgments and audit adjustment decisions historically accurate internal evidence suggesting less conservative collectability judgments and audit adjustment decisions than unfavorable external and less credible internal evidence. The author also found evidence that communication personalization elicits affect and increases effort levels for purposes of improving pre-task information processing and subsequent collectability judgments. The results also indicate that the influence of perceived personalization may not incrementally influence reliance on internal evidence that is consistent with unfavorable external evidence.

To the best of the author’s knowledge, this is the first study to examine auditor reliance behaviors when internal evidence suggests a less conservative collectability judgment and audit adjustment decision than unfavorable external evidence and is the first to explore communication personalization as a tool for effective audit knowledge transfer.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

This study aims to explore whether parents perceive themselves as responsible for helping their teenage children (aged 13–17 years) cope with the persuasive effects of personalized advertising and the related process of online data collection. In addition, this study aims to examine how this parental responsibility is shaped.

A survey among parents (N = 354) of teenagers between the ages of 13 and 17 years was conducted.

Exploratory analyses showed that parents are highly concerned about their teens’ exposure to personalized advertising and online data collection, and that they consider themselves and the commercial companies behind these practices as responsible for protecting teenagers. Furthermore, the study showed that parents who believe that their children have higher levels of media skills presume less negative and more positive influences of personalized advertising on their children. The presumed negative influences increase parental concerns and responsibility, while presumed positive influences decrease parental concerns and responsibility.

Most previous studies on personalized advertising and online data collection were conducted among the teenagers themselves or discussed the regulatory framework concerning this topic. This study, however, focuses on one of the most important socialization agents that could help teenagers cope with these practices. To the best of the authors’ knowledge, this is one of the first studies to examine parents’ views regarding personalized advertising targeting teenagers and it provides insights in how parents perceive their own responsibility.

The purpose of this study is to build a personalized learning intervention system, which can support students' personalized learning, improve teachers' teaching efficiency and students' learning effect.

The research proposes a personalized learning intervention method based on a collaborative filtering algorithm and knowledge map. The application of knowledge map makes learning content organized, and the use of collaborative filtering algorithm makes it possible to provide personalized learning recommendations for students. This personalized learning intervention system can monitor students' learning development and achieve the combination of personalized and efficiency. For the study, 152 seventh graders were assigned to a control group and an experimental group. Traditional learning intervention was used in the control group, and individualized learning intervention was used in the experimental group.

SPSS was used for data organization and analysis. The effectiveness of the personalized learning intervention system is verified by quasi-experimental research, and the influence of the system on students' learning effect is discussed. The result found that personalized learning interventions were more effective than traditional approaches in improving students’ achievement. However, for students of different learning levels, personalized learning intervention system has different effects on learning confidence and learning anxiety.

The personalized learning intervention system based on the collaborative filtering algorithm and knowledge map is effective in improving learning effect. And, it also has a certain influence on students' psychology.

This paper aims to shed light on the impact of personalized news media on the shared issue agenda that provides democracies with a set of topics that structure the public debate. The advent of personalized news media that use smart algorithms to tailor the news offer to the user challenges the established way of setting the agenda of such a common core of issues.

This paper tests the effects of personalized news use on perceived importance of these issues in the common core. In particular, the authors study whether personalized news use leads to a concentration at the top of the issue agenda or to a more diverse issue agenda with a long tail of topics.

Based on a cross-sectional survey of a representative population sample (n = 1,556), we find that personalized news use does not lead to a small common core in which few topics are discussed extensively, yet there is a relationship between personalized news use and a preference for less discussed topics. This is a result of a specific user profile of personalized news users: younger, more educated news users are more interested in topics at the fringes of the common core and also make more use of personalized news offers.

The results are discussed in the light of media diversity and recent advances in public sphere research.

This paper contributes to the ongoing debate about algorithmic news dissemination. While, currently, much attention is reserved for the role of platforms as information gatekeepers in relationship to the news media, maybe their ability to enable or hinder the audience in discovering and distributing news content is part of what really characterizes their influence on the market place of ideas.

The best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.

The data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.

The result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.

The paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.