How are major gambling brands using Twitter?

ABSTRACT

This paper is the first to compare how major gambling brands are using the popular social media platform Twitter, looking at how gambling brands vary in the frequency of their messages, the content of their tweets and engagement with their Twitter activity. 63,913 tweets were collected from seven well-known British gambling brands (Bet365, Betfair, Betfred, Coral, Ladbrokes, Paddy Power, William Hill) and their associated Twitter accounts (Total Number of Accounts = 22) via the Twitter Application Program Interface (API) on the 1 August 2018. Companies varied in their approach to Twitter, some posting from a single account whereas others segmented their tweets by topic or purpose. Frequency analysis of tweets showed that on average major gambling brands tweeted anywhere between 89 and 202 tweets a day. Sentiment analysis of tweets showed a positivity bias with the language in tweets being associated with positive emotions like anticipation, trust and joy. Paddy Power, Bet365 and Coral produced the content that received the highest number of likes or shares from other twitter users. This study highlights the extent to which companies are using Twitter; followers could potentially be receiving hundreds of messages per day.     

The Baby Boomer Effect: Changing Patterns of Substance Abuse Among Adults Ages 55 and Older

Between now and 2030, the number of adults aged 65 and older in the United States will almost double, from around 37 million to more than 70 million, an increase from 12% of the U.S. population to almost 20%. It was long held that, with only a few isolated exceptions, substance abuse simply did not exist among this population. In light of the impact of the baby boom generation, this assumption may no longer be valid. The authors examined admissions of persons 55 years and older (n = 918,955) from the Treatment Episode Data Set (1998–2006). Total admissions with a primary drug problem with alcohol have remained relatively stable over this time. Admissions for problems with a primary drug other than alcohol have shown a steady and substantial increase. Clearly, data from the Treatment Episode Data Set indicate a coming wave of older addicts whose primary problem is not alcohol. The authors suspect that this wave is led primarily by the continuing emergence of the baby boomer generation.     

Failure Modes and Effects Analysis: An Evaluation of Group versus Individual Performance

Failure modes and effects analysis (FMEA) is one of the most frequently used tools in process and product design: it is used in quality and reliability planning, and event and failure mode analysis. It has a long history of use and is a formally prescribed procedure by a number of prominent standards organizations. In addition, it's popular use has evolved as a less formal and widely interpreted tool in the area of Lean/Six Sigma (LSS) process improvement. This paper investigates one of the most important issues related to FMEA practice—the quality of individual vs. group performance in ranking failure modes. In particular, we compare FMEA rankings generated by: (i) individuals, (ii) group consensus, and (iii) non‐collaborative aggregation of group input (a synthesized group ranking). We find that groups outperform individuals and that synthetic groups perform as well as group consensus. We explain the implications of this result on the coordination of the design of products and processes amongst distributed organizations. The increasing distribution of product design efforts, both in terms of geography and different organizations, presents an opportunity to improve coordination using distributed synthetic group‐based FMEA.     

A Common Situation Conducive to Bizarre Distribution Shapes

Conditions conducive to bizarre-shaped distributions are fairly common in certain areas of research where, for perfectly valid reasons, an important causal variable is left uncontrolled. Theoretical rationale and actual examples are given to show how such distributions are generated, to exemplify their shapes, and to indicate their prevalence in practice. An L-shaped type tends to occur when time scores are recorded for a task subject to infrequent but time-consuming errors; other types occur when measuring behavior influenced by social conformity, or under other circumstances. The L shape appears to be far more conducive to nonrobustness than are previously investigated shapes.     

Unpacking Team Familiarity: The Effects of Geographic Location and Hierarchical Role

Examination of team productivity finds that team familiarity, i.e., individuals' prior shared work experience, can positively impact the efficiency and quality of team output. Despite the attention given to team familiarity and its contingencies, prior work has focused on whether team members have worked together, not on which team members have worked together, and under what conditions. In this paper, I parse overall team familiarity to consider effects of geographic location and the hierarchical roles of team members. Using data on all software‐development projects completed over 3 years at a large Indian firm in the global outsourced software services industry, I find that team familiarity gained when team members work together in the same location has a significantly more positive effect on team performance compared with team familiarity gained while members were collaborating in different locations. Additionally, I find that hierarchical team familiarity (a manager's experience with front‐line team members) and horizontal team familiarity (front‐line team members' experience gained with one another) have differential effects on project team performance. These findings provide insight into the relationship between team experience and team performance.     

A developmental, biopsychosocial model for the treatment of children with gender identity disorder

This article provides a summary of the therapeutic model and approach used in the Gender Identity Service at the Centre for Addiction and Mental Health in Toronto. The authors describe their assessment protocol, describe their current multifactorial case formulation model, including a strong emphasis on developmental factors, and provide clinical examples of how the model is used in the treatment.     

Generalized gamma parameter estimation and moment evaluation

The generalized gamma distribution includes the exponential distribution, the gamma distribution, and the Weibull distribution as special cases. It also includes the log-normal distribution in the limit as one of its parameters goes to infinity. Prentice (1974) developed an estimation method that is effective even when the underlying distribution is nearly log-normal. He reparameterized the density function so that it achieved the limiting case in a smooth fashion relative to the new parameters. He also gave formulas for the second partial derivatives of the log-density function to be used in the nearly log-normal case. His formulas included infinite summations, and he did not estimate the error in approximating these summations.

We derive approximations for the log-density function and moments of the generalized gamma distribution that are smooth in the nearly log-normal case and involve only finite summations. Absolute error bounds for these approximations are included. The approximation for the first moment is applied to the problem of estimating the parameters of a generalized gamma distribution under the constraint that the distribution have mean one. This enables the development of a correspondence between the parameters in a mean one generalized gamma distribution and certain parameters in acoustic scattering theory.     

Testing for empty sets when misclassifications may be present

A test for empty sets is described. This new test is suitable for testing for mutual exclusivity, sets being nested, and many other configurations. It might be hypothesized that two medical conditions do not both occur in individuals. This can be expressed as an empty intersection of the two sets of people with each condition. An important feature of the test is the incorporation of misclassification rates into the analysis. The test utilizes the potential misclassifications for false designations in the sets that are hypothesized to be empty. The test is quite powerful. When the null hypothesis is rejected, follow-up tests on one or more sets that are included in the null hypothesis can be performed using the same new test. MatLab code is supplied.     

Re-distributed manufacturing and the food-water-energy nexus: opportunities and challenges

Abstract

Addressing the intersection of two important emerging research areas, re-distributed manufacturing (RDM) and the food-energy-water (FEW) nexus, this work combines insights from engineering, business and policy perspectives and explores opportunities and challenges towards a more localized and sustainable food system. Analysis centred on two specific food products, namely bread and tomato paste reveals that the feasibility and potential of RDM vary with the type of food product and the supply chain (SC) components. Physically, energy efficiency, water consumption and reduction of waste and carbon footprint may be affected by scale and location of production activities and potentials of industrial symbiosis. From the business perspective, novel products, new markets and new business models are expected in order for food RDM to penetrate within the established food industry. Studies on policies, through the lens of public procurement, call for solid evidence of envisioned environmental, social and economic benefits of a more localized food system. An initial integrated framework is proposed for understanding and assessing food RDM and the FEW nexus.