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Psychological elder abuse (PEA) assessment is described with different thresholds. The purpose of this paper is to examine how the prevalence of PEA and the phenomenon’s characterisation varied using two different thresholds.

Participants from the cross-sectional population-based study, Aging and Violence (n=1,123), answered three questions regarding PEA. The less strict measure considered PEA as a positive response to any of the three evaluated behaviours. The stricter measure comprised the occurrence, for more than ten times, of one or more behaviours. A multinomial regression compared cases from the two measures with non-victims.

Results show different prevalence rates and identified perpetrators. The two most prevalent behaviours (ignoring/refusing to speak and verbal aggression) occurred more frequently (>10 times). Prevalence nearly tripled for “threatening” from the stricter measure (>10 times) to the less strict (one to ten times). More similarities, rather than differences, were found between cases of the two measures. The cohabiting variable differentiated the PEA cases from the two measures; victims reporting abuse >10 times were more likely to be living with a spouse or with a spouse and children.

Development of a valid and reliable measure for PEA that includes different ranges is needed.

The study exemplifies how operational definitions can impact empirical evidence and the need for researchers to analyse the effect of the definitional criteria on their outcomes, since dichotomization between victim and non-victim affects the phenomenon characterisation.

The purpose of this paper is to examine, through a qualitative lens, how community elder abuse and the ageing process are represented in the older adults’ narratives reporting abuse perpetrated by family members.

A qualitative study of a convenience sample of 22 interviews from 24 older adults (two couples) aged 60 years or older who had experienced one or more types of abuse and had sought help about the victimisation experience. A general inductive approach of thematic content analysis was employed.

The four main emergent themes related to the passage of time or the perception of becoming old within the process of abuse were: abuse grown old, abuse after entering later life, vulnerability to abuse and responses to abuse. Ageing was found to be associated with an increase vulnerability to abuse and an important element in shaping how older adults experience, report and cope with victimization. The social and contextual issues of being older also influenced the decision of ending (or not) the abuse and the victims’ repertoire of responses.

Despite the little suitability of chronological age to define and delimit elder abuse, understanding the phenomenon demands the recognition of ageing (both as a process and as a product) in order to more accurately identify aetiology processes and develop interventions.

Curves with various profiles have been demonstrated to be more attractive and decorative than the straight lines by William Hogarth. Among all kinds of curves, Hogarth proposed seven serpentine lines as the most beautiful curves, i.e., Hogarth curves. Those seven Hogarth curves are subsequently applied in a wide range of fields, e.g., sculpture, painting, architecture and fashion design, indicating their significance to the development of the formal beauty. Recently, the beauty of Hogarth curves has been suspected to be induced by their special-designed curvature, which could have the potential relationship with the Golden Ratio. The purpose of this paper is to explore the relationship between the Hogarth curves and golden ratio by comparing the curvature of curves with the Fibonacci sequence.

Each of the Hogarth curves was fully restored and divided into two parts according to the turning point of the curvature; the ratios of span, curvature and angles between these two parts were compared with the Fibonacci sequence.

The experimental results disclosed that the ratio of the fourth Hogarth curve, which was considered as the most beautiful line by Hogarth, was infinitely approaching the golden ratio. Based on the relationship between the fourth Hogarth curve and the golden ratio, the ratios of each curve were employed to define and normalize these curves, providing a quantitative way to redraw the Hogarth curves.

This research work unlocked the information of the relationship between the Hogarth curves and golden ratio, and proposed an effective and convenient mathematic way to quantify the Hogarth curves. The experimental findings disclosed the underlying mechanisms of the beauty of the forth Hogarth curves. Such a fundamental study will promote the establishment of the normalized methods for evaluating the beauty of arts and provide novel ideas for researchers and industrial technologists to use the Hogarth curves.

The purpose of this paper is to examine the artificial intelligence (AI) methodologies to fight against money laundering crimes in Colombia.

This paper examines Colombian money laundering situations with some methodologies of network science to apply AI tools.

This paper identifies the suspicious operations with AI methodologies, which are not common by number, quantity or characteristics within the economic or financial system and normal practices of companies or industries.

Access to financial institutions’ data was the most difficult element for research because affect the implementation of a set of different algorithms and network science methodologies.

This paper tries to reduce the social and economic implications from money laundering (ML) that result from illegal activities and different crimes against inhabitants, governments, public resources and financial systems.

This paper proposes a software architecture methodology to fight against ML and financial crime networks in Colombia which are common in different countries. These methodologies complement legal structure and regulatory framework.

The contribution of this paper is how within the flow already regulated by financial institutions to manage the ML risk, AI can be used to minimize and identify this kind of risk. For this reason, the authors propose to use the graph analysis methodology for monitoring and identifying the behavior of different ML patterns with machine learning techniques and network science methodologies. These methodologies complement legal structure and regulatory framework.

The chapter analyzed the knowledge transfer processes of the Latin American academy to the actors in its environment, and the Science, Technology, and Innovation policy that facilitates or inhibits the processes of generation and use of this university knowledge. The cases of Argentina, Brazil, Chile, Colombia, and Mexico were analyzed to understand the practice of the university knowledge transfer model at different levels – strategic, organizational, and operational – and the complexities involved in the process. It was evidenced by the urgent demand for the transformation of the Latin American University through sustainability and digitalization approaches to be a catalyst for development in the region. The chapter closes with a critical analysis of the phenomenon, future lines of research, and implications of the praxis.

Throughout centuries, Latin America has faced a paradox. On one hand, the abundance of resources has attracted immigrants who find a suitable place to undertake profitable business ventures in the region. On the other hand, the limited entrepreneurial resources of most countries of the region have motivated the migration of talented people, among them entrepreneurs, to non-Latin American countries. This chapter explores this paradox through the analysis of entrepreneurs' motivations to immigrate to, and migrate from Latin America, the influence of their profile on their business ventures, as well as the role played by both the home and the host countries' institutional conditions. The findings of this analysis underline the diversity of migrant entrepreneurs in terms of personal resources and survival versus opportunity-driven migration decisions. They also reveal the kind of mechanisms migrant entrepreneurs use to counteract their host country's institutional challenges.

This chapter studies how entrepreneurship research has evolved at the Latin American level. Using the database available in the Web of Science Core Collection, a period-by-period bibliometric analysis is carried out to identify possible changes in research trends over time. On the one hand, the results show that in the period 2000–2006, research focuses mainly on rural development, community development and financial resources. On the other hand, in the period 2007–2013, the research priority is related to international entrepreneurship, private capital raising and studies based on the Global Entrepreneurship Monitor (GEM). Finally, during the period between 2014 and 2020, research focused mainly on self-employment, family businesses, promotion of angel investor networks, venture capital, and female entrepreneurship. The results of this study may be of interest to academics, researchers, and policymakers to understand the evolution of this topic in recent decades and to promote entrepreneurship and innovation in the region.

From the perspective of business history, this chapter presents an overview of the development of the tourism sector in South America, placing special emphasis on the Peruvian case. The chapter explores various topics related to the tourism chain, such as hotel networks, the role of the state, tour operators, micro- and small enterprises, linkages between tourism and sustainability, the formation of clusters in the sector, and interactions between different entrepreneurs in the chain. Special emphasis is placed on the Peruvian case, especially when it comes to discussing the role of micro- and small enterprises in the sector.

The purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).

The paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.

Critic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.

This study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.

The major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.