treatment, and promote mental health and well-being resources, appropriate new technology, and financial services, including microfinance

Sustainable Development Goals

Modern information technology now allows the easy collection and storage of massive amounts of data. This development presents tremendous opportunities and challenges for scientists and statisticians. Traditionally, longitudinal study designs have most often used relatively short series of measurements through time (e.g., ≤10 measurement occasions), and statistical methodology for longitudinal data has concentrated on this case. However, recent technological innovations have led to the increasing popularity of studies that generate what Walls and Schafer term “intensive longitudinal data” (ILD). Such data, stemming from many (typically frequent) measurement occasions through time on a sample of subjects, arise in various disciplines. However, it is their growing prevalence in psychology and related areas of social science that has provided the primary motivation for the publication of Models for Intensive Longitudinal Data. This edited volume contains a collection of chapters that describe and illustrate statistical models and methodology appropriate for ILD. The contributing authors are mostly social scientists and statisticians, with many of the chapters written collaboratively across these disciplines. The emphasis is on explaining various classes of models: their definition, conceptualization, interpretation, relationship to other model classes, and usefulness for addressing questions of scientific interest. Technical, theoretical, and other aspects of limited interest to practitioners are kept to a minimum and, in some cases, relegated to appendixes. Nearly all of the chapters include applications that are pursued further than is typical in statistical publications, with novel substantive results presented in many cases. Topics represented in the applications include smoking behavior, patterns of alcohol use, relationship of feelings of control and choice over daily activities, marital intimacy, daily patterns of personality characteristics, neural mechanisms of attentional control, and traffic intensity. After a nicely written introduction that motivates, identifies themes in the analysis of ILD, and summarizes the chapters, the editors and their collaborators contribute two chapters that introduce and summarize the main ideas of two traditional methodologies for longitudinal data: the linear mixed model (also called multilevel model or hierarchical linear model) and generalized estimating equations. This material sets the stage for subsequent chapters that discuss functional multilevel models, item response models and their relationship to mixed models, semiparametric varying-coefficient linear mixed models, multilevel autoregressive models, state-space models, dynamical systems models based on differential equations, and point process models. A final chapter highlights some emerging technologies for the collection of ILD and discusses the associated statistical challenges. All of the chapters are written at an accessible level. Collectively, they do a nice job of bridging the gap between the social sciences and the field of statistics, clarifying the relationship between methodology and terminology used in the two areas while introducing a broad array of models and methods for ILD analysis. Although this volume will be of greatest value to quantitatively oriented researchers in psychology and other areas of social science, I also recommend it to statisticians and anyone else interested in the collection and analysis of ILD.

Models for Intensive Longitudinal Data

Sharing economy benefits and sustainable development goals: Empirical evidence from the transportation industry of Vietnam

The United Nations Sustainable Development Goals (SDGs) offer a universal framework for addressing pressing global economic, environmental, and social challenges. Central to the SDG agenda is the vital role of business innovation in fostering positive change and promoting the triple bottom line of sustainability: people, planet, and prosperity. Many years have passed following the SDGs’ inception, yet the engagement of business innovation scholarship with the SDGs remains sparse. Moreover, a comprehensive review examining the convergence of business, innovation, and sustainability through the lens of the SDGs is notably absent, constraining insights into present advancements and prospective trajectories. In this regard, this study adopts the SDGs as a lens to investigate the contribution of business innovation to sustainable development challenges and its impact on the SDG agenda. To do so, this study conducts a systematic literature review, incorporating a bibliometric analysis of articles published in top-tier business journals, to offer an integrated overview of current trends in business, innovation, and sustainability research through the lens of the SDGs. In addition, this study performs a content analysis to consolidate and synthesize relevant research in this space, resulting in a multi-dimensional framework that illustrates the intricate, dynamic interplay between the drivers, dimensions, and impacts of business innovation on sustainability and their relationship with the SDGs. In doing so, this study accentuates extant gaps, delineates profound implications, and proposes meaningful directions to advance the understanding of the intersection between business, innovation, and sustainability in the context of the SDGs. 

Convergence of business, innovation, and sustainability at the tipping point of the sustainable development goals

The prime concern of this study is to explore how technology influences entrepreneurial marketing decisions during the world pandemic (COVID-19). The study uses a sample of 127 SMEs from Bangladesh. Data were collected by sending out questionnaires electronically and by mail. Smart PLS (SEM) 3.0 was used to analyse the data following the quantitative method. The study reveals positive and significant relationships between entrepreneurial opportunity recognition, opportunity development and opportunity exploitation with their entrepreneurial marketing decisions. It also claims that entrepreneurial passion mediates the relationship between entrepreneurial opportunity recognition and opportunity development with their entrepreneurial marketing decisions. However, entrepreneurial passion does not mediate the relationship between entrepreneurial opportunity exploitation and entrepreneurial marketing decisions. The study offers researchers a broader and more wide-ranging view of the importance of artificial intelligence in small firms. Researchers, educators and practitioners will benefit from the findings. The analyses are more complex and varied than the methodologies used in most of the limited previous research.

/pdf/technology-and-entrepreneurial-marketing-decisions-during-265h9ob43n.pdf

Technology and Entrepreneurial Marketing Decisions During COVID-19

The proxemics of social interactions (e.g., body distance, relative orientation) influences many aspects of our everyday life: from patients' reactions to interaction with physicians, successes in job interviews, to effective teamwork. Traditionally, interaction proxemics has been studied via questionnaires and participant observations, imposing high burden on users, low scalability and precision, and often biases. In this paper we present Protractor, a novel wearable technology for measuring interaction proxemics as part of non-verbal behavior cues with fine granularity. Protractor employs near-infrared light to monitor both the distance and relative body orientation of interacting users. We leverage the characteristics of near-infrared light (i.e., line-of-sight propagation) to accurately and reliably identify interactions; a pair of collocated photodiodes aid the inference of relative interaction angle and distance. We achieve robustness against temporary blockage of the light channel (e.g., by the user's hand or clothes) by designing sensor fusion algorithms that exploit inertial sensors to obviate the absence of light tracking results. We fabricated Protractor tags and conducted real-world experiments. Results show its accuracy in tracking body distances and relative angles. The framework achieves less than 6° error 95% of the time for measuring relative body orientation and 2.3-cm - 4.9-cm mean error in estimating interaction distance. We deployed Protractor tags to track user's non-verbal behaviors when conducting collaborative group tasks. Results with 64 participants show that distance and angle data from Protractor tags can help assess individual's task role with 84.9% accuracy, and identify task timeline with 93.2% accuracy.

/pdf/measuring-interaction-proxemics-with-wearable-light-tags-5ckeey2si2.pdf

Measuring Interaction Proxemics with Wearable Light Tags

Public information search data from sources such as Google Trends affords researchers a perspective on what society does not know, or what society wants to find out prompted by, or in response to, developments in societal communication and news media events. In times of crisis, online public information search thus offers a window into the most urgent concerns of, and demands by society. By extension, such data offers a window into upcoming, and currently pressing demands on businesses, policymakers and researchers. This research note aims to illustrate some of these themes with a visual-based overview of the current COVID-19 / Coronavirus crisis.

Online Information Search During COVID-19

A Note on Data-driven Actor-differentiation and SDGs 2 and 12: Insights from a Food-sharing App

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely questioned by policymakers. We demonstrate a data-driven approach to address this issue, deriving national estimates of food insecurity via combination of supervised machine learning with network analysis of user behaviour, extracted from the world’s most popular peer-to-peer food sharing application (OLIO). Despite long-standing theoretical links between social graph topologies and physical neighbourhoods, prior research has not considered dimensions of geography, network interactions and behaviours in the digital/analogue space simultaneously. In addressing this oversight, we produce a browser-based, interactive and rapidly updateable visualisation, which can be used to analyse the spatial distribution of food insecurity across the UK, and provide new perspective for policy research.

/pdf/fims-identifying-predicting-and-visualising-food-insecurity-1b9llve4n4.pdf

FIMS: Identifying, Predicting and Visualising Food Insecurity

In this working paper, we investigate high-level changes in the online strategic communications of organizations engaged with SDG 8.7 (ending modern slavery) during the COVID-19 crisis. We present preliminary evidence of important semantic and thematic shifts based on data from Twitter during this time, with an emphasis on developing the SOLACE (Social Listening and Communications Engagement) dashboard, and with recommendations for important future research involving the use of social media data as a basis for distilling organizational-agenda proxies based on digital campaigns and activism during times of crisis.

Dashboarding The Online Strategic Communications of Anti-slavery Organizations During COVID-19

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