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The purpose of this study is to provide broader understanding of the significant role that the pension system has in the Spanish economy by estimating the sectoral production, employment and income sustained by pensioners' consumption.

Based on input–output tables by the World Input–Output Database and consumption data from the Household Budget Survey by the Spanish Statistical Office, a demoeconomic model is applied to quantify the direct impacts, indirect impacts from interindustry links and induced impacts from income–consumption connections over a nine-year period (2006–2014). Then, the factors driving the evolution of total output, employment and value added during such period have been examined by using structural decomposition analysis.

The growing participation of consumption by pensioner households in final demand had proven crucial during the 2008 crisis to alleviate the negative trend in production and employment derived from the collapse in consumption suffered by the rest of households.

Determining the underlying factors driving changes in both employment and income during the 2008 crisis can be of interest in political decision-making on the sustainability of the Spanish pension system.

The results of estimating both the employment and income supported by pensioners' consumption reveal the significant stabilizing effect of the public spending on pensions, particularly during the 2008 crisis.

The current Spanish approach of attaining the pension system sustainability by merely reducing social protection costs ignores the adverse consequences of a lower pensioners' demand. This paper addresses an alternative view in which pension spending is not considered a burden on economic growth but rather a means of improving the level of production and employment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2019-0047

This study identifies measures to recover economic growth and build sustainable societies and markets in post-COVID-19 scenarios – with a perspective of resilience and adaptability to climate change and massive biodiversity loss. Additionally, this study uncovers the interventions implemented to address economic, environmental and social consequences of past crises based on a systematic literature review. Specifically, this chapter provides answers to the following six questions:

1. What has been done in the past to rebuild social, economic and environmental balance after global crises?

2. Where (geographical region) did the analysis on measures taken concentrate?

3. When have scholars analysed past measures to rebuild business and society after a global crisis?

4. How did the past measures to rebuild business and society after the global crisis take place?

5. Who promotes the measures to rebuild business and society after a global crisis takes place?

6. Why is it important to study the previous literature on past measures to rebuild business and society after a global crisis takes place?

What has been done in the past to rebuild social, economic and environmental balance after global crises?

Where (geographical region) did the analysis on measures taken concentrate?

When have scholars analysed past measures to rebuild business and society after a global crisis?

How did the past measures to rebuild business and society after the global crisis take place?

Who promotes the measures to rebuild business and society after a global crisis takes place?

Why is it important to study the previous literature on past measures to rebuild business and society after a global crisis takes place?

Finally, this chapter identifies future research opportunities to rebuild business and society after the past global crises.

Recognizing the need to build global-minded citizens, higher education institutions are increasingly trying to find ways to leverage their international programs to develop students’ intercultural competence. The MA in global leadership at Royal Roads University, Canada, created an international partnership in Ecuador that serves to go beyond the traditional student study abroad or service learning focus and instead focuses on developing competencies of global mindedness and strategic relationships. In this chapter, we present an analysis of how an international student group engaged in building dynamic partnerships within a Global South country to create change for sustainable development initiatives of mutual concern. Through a case example, we describe how these partnerships evolved and adapted in ways that enhanced the learning needs of the students while simultaneously supporting the development of new educational opportunities for Ecuadorians. To illustrate, this chapter delineates the activities that members of the program undertook to connect and develop a mutuality of relationship across diverse stakeholders in Ecuador. The authors analyze this network-building process from the perspective of cultural context, building trust and influence, and responding to social development needs of host communities.

This paper aims to provide a comprehensive review of research on robotics in travel, tourism and hospitality, and to identify research gaps and directions for future research.

This paper analyzes 131 publications published during 1993-2019, identified via Scopus, Web of Science, ResearchGate, Academia.edu and Google Scholar. It offers quantitative analysis of frequencies and cross-tables and qualitative thematic analysis of the publications within each of seven identified domains.

The paper identifies “Robot,” “Human,” “Robot manufacturer,” “Travel/tourism/hospitality company,” “Servicescape,” “External environment” and “Education, training and research” as the research domains. Most research studies are dedicated to robots in restaurants, airports, hotels and bars. Papers tend to apply engineering methods, but experiments and surveys grow in popularity. Asia-Pacific countries account for much of the empirical research.

The analysis was limited to publications indexed in four databases and one search engine. Only publications in English were considered. Growing opportunities for those who are anxious to publish in the field are identified. Importantly, emerging research is branching out from the engineering of robots to the possibilities for human/robot interactions and their use for service providers, opening up new avenues of research for tourism and hospitality scholars.

The paper identified a myriad of application areas for robots across various tourism and hospitality sectors. Service providers must critically think about how robots affect the servicescape and how it needs to be adjusted or re-imagined to ensure that robots and employees can augment the service experiences (co-)created within it.

This is the first study to systematically analyze research publications on robotics in travel, tourism and hospitality.

本论文全面评论了在旅游酒店业中的机器人技术的研究, 并指出文献缺口和未来研究方向。

本论文分析了在1993年至2019年发布在Scopus、Web of Science、ResearchGate、Academia.edu、和Google Scholar的131篇文献。本论文对文献做了一系列定量分析, 包括频率分析、交叉表、定性文本分析、在七大确立的领域中对每个领域的文献进行分析。

本论文确立了七个研究领域：机器人、人类、机器生产者、旅游酒店企业、Servicescape、外部环境、和教育培训和研究。大多数文献集中在对饭店、机场、酒店、和酒吧的机器人研究。文献往往采用工程手段进行研究, 但是实验和问卷方式正在呈增长趋势。亚太国家占据大多数实证研究作品。

本论文样本库局限于四个数据库和一个搜索引擎。只有英文文献被采样。本论文为对相关领域感兴趣的学者指出研究方向。重要的是, 本论文发现用工程角度研究机器人的文献有了分支, 有一小部分文献开始着手研究人/机器人交互和其在服务过程中的使用的研究, 这对旅游酒店学者提供新研究视角。

本论文指出了一系列有关旅游酒店领域中机器人的应用。服务商必须重视机器人如何影响Servicescape以及如何审视机器人与人的交互, 确保其与员工加强消费者的服务体验（价值共创）。

本论文是首篇系统评论旅游酒店领域中机器人研究文献的文章。

关键词：机器人、机器人经济、机器人设计、机器人使用、Servicescape、rService、人-机器人交互、研究议程

An increasing number of migrants and refugees seeking asylum in Germany is challenging psychiatrists and psychotherapists in multiple ways. Different cultural belief systems on the causes of mental illness and their treatment have to be taken into consideration. The purpose of this study is to explore perceived causes of depression among Farsi-speaking migrants and refugees from Afghanistan and Iran, which represent two groups with a shared cultural heritage, but originating from very different regimes of mobility. Both are among the largest migrant groups coming to Germany over the past decade.

In total, 50 Iranian and 50 Afghan migrants and refugees, who arrived in Germany in the past 10 years were interviewed, using an unlabeled vignette presenting signs and symptoms of depression. The answers were then coded through inductive content analysis.

Among Iranians, there was a more significant number of causal attribution to Western psychiatric concepts, whereas Afghans attributed depression more often to the experience of being a refugee without referring to psychological concepts. These differences in attribution did, however, not affect the desire for a social distance toward depressed people. Nonetheless, a higher number of years spent in Germany was associated with less desire for social distance toward persons with depression among Afghans, but not among Iranians.

To the best of the knowledge, this is the first study examining perceived causes of depression with Farsi-speaking migrants in Germany and contributes to understanding tendencies in the perception of depression in non-Western migrant groups.

This study aims to investigate the effects of brand familiarity on attitude formation across different advertising channels, product types and brand settings.

A meta-analysis containing 107 empirical studies with 183 effects sizes tests a theoretical model according to situational moderators and methodological factors of brand familiarity.

Brand familiarity has stronger positive impacts on attitude formation under particular advertising tools (online and real advertising), product types (hedonic and mature products) and brand characteristics (memory-based recall). The findings also depend on methodological factors such as student samples, laboratory settings and non-estimated effect sizes.

This meta-analytic study reconciles prior inconsistencies and advances the understanding of brand familiarity across key advertising, product and brand moderators.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.