Online information search by people with Multiple Sclerosis: A systematic review.

BACKGROUND: People with Multiple Sclerosis (pwMS) search for information online about various aspects of living with their disease, but details about patterns of searching and outcomes are unclear. This means that opportunities to leverage online resources to support pwMS, and to enhance shared decision making, may be missed. We aimed to do a systematic review of the literature on digital information searching by pwMS. METHODS: We performed a systematic search for studies assessing online information seeking of pwMS in MEDLINE and JSTOR databases. Studies were screened and selected by two investigators. All study designs were included, risk of bias was assessed using the Critical Appraisal Skills Programme qualitative checklist. Reports were assessed for the proportion of patients searching information online about MS, type of information sought, online tools used by patients, perceived quality of the information acquired, and impact of online searching in pwMS. RESULTS: We identified 5 studies, including 10,090 patients. Most pwMS search for information online (53.8-82 %), which they rarely discuss with physicians. The most common topics are treatment, general disease information, symptoms, lifestyle recommendations, prognosis, and coping strategies. Patients that are younger, have a shorter disease duration, primary progressive MS, and during periods of disease worsening, are more likely to use online resources. Online information is perceived as low quality by pwMS. CONCLUSIONS: Online information search is prevalent among pwMS. Despite concerns with the quality of the available information, only a minority of pwMS will discuss the information found with their physician. These findings highlight the importance of developing and providing quality online information resources for pwMS.

Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment.

Artificial intelligence (AI) is expected to bring to the physical retail environment the kind of mass personalisation that is already common in online commerce, delivering offers that are targeted to each customer, and that adapt to changes in the customer's context. However, factors related to the in-store environment, the small screen where the offer is delivered, and privacy concerns, create uncertainty regarding how customers might react to highly personalised offers that are delivered to their smartphones while they are in a store. To investigate how customers exposed to this type of AI-enabled, personalised offer, perceive it and respond to it, we use the personalisation-privacy paradox lens. Case study data focused on UK based, female, fashion retail shoppers exposed to such offers reveal that they seek discounts on desired items and improvement of the in-store experience; they resent interruptions and generic offers; express a strong desire for autonomy; and attempt to control access to private information and to improve the recommendations that they receive. Our analysis also exposes contradictions in customers' expectations of personalisation that requires location tracking. We conclude by drawing an analogy to the popular Snakes and Ladders game, to illustrate the delicate balance between drivers and barriers to acceptance of AI-enabled, highly personalised offers delivered to customers' smartphones while they are in-store.

Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective.

Financial services organisations facilitate the movement of money worldwide, and keep records of their clients' identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task. We find that, due to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering.

Stakeholders of the world, unite!: Hospitality in the time of COVID-19.

The COVID-19 pandemic created a global, complex crisis, without a clear end in sight, presenting an existential threat to many hospitality businesses. Drawing on stakeholder theory, we develop a framework for recovery strategy development for COVID-19, which engages salient stakeholders in the process of recognizing challenges, rationalizing changes needed and refashioning ways of working. The framework is used to analyze the process of development of a recovery strategy for a boutique hotel in England, UK, via a case study methodology. The analysis brings to the fore the interdependencies between the hotel owners and its employees, customers, governments, suppliers and communities, at local, national and international levels. Moreover, the analysis shows how collaborating with these stakeholders leads to the identification of revenue streams for the hotel, operational modifications and even the development of new commercial partnerships.