Establishment-level occupational safety analytics: Challenges and opportunities.

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the "big" data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.

Establishment-level safety analytics: a scoping review.

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Advancing safety analytics: A diagnostic framework for assessing system readiness within occupational safety and health.

Big data and analytics have shown promise in predicting safety incidents and identifying preventative measures directed towards specific risk variables. However, the safety industry is lagging in big data utilization due to various obstacles, which may include lack of data readiness (e.g., disparate databases, missing data, low validity) and personnel competencies. This paper provides a primer on the application of big data to safety. We then describe a safety analytics readiness assessment framework that highlights system requirements and the challenges that safety professionals may encounter in meeting these requirements. The proposed framework suggests that safety analytics readiness depends on (a) the quality of the data available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure, including technological platforms and skills required for data collection, storage, and analysis of health and safety metrics, and (d) measurement culture, or the emergent social patterns between employees, data acquisition, and analytic processes. A safety-analytics readiness assessment can assist organizations with understanding current capabilities so measurement systems can be matured to accommodate more advanced analytics for the ultimate purpose of improving decisions that mitigate injury and incidents.

Do employees' work schedules put them at-risk? The role of shift scheduling and holidays in predicting near miss and incident likelihood.

INTRODUCTION: Using crew scheduling and injury incident data from a Fortune 500 manufacturing company, this study analyzed the effect of consecutive shifts and shifts near holidays on near misses and incidents. METHODS: Logistic regressions were conducted with consecutive workdays, days near holidays, and time of shift as predictors of incident and near miss outcomes. RESULTS: The logistic regression analysis indicated that working consecutive day shifts increases the probability of an incident occurring, with the fourth consecutive shift resulting in the most risk. The consecutive shift pattern did not replicate to employees working the night shift. However, the first and second shifts when transferring to a night schedule appear to have a greater chance of incident. Shifts near holidays did not have a significantly higher risk than other shifts. PRACTICAL APPLICATION: The current research suggests that organizations can use similar analytic techniques to determine if shift scheduling might be related to increased risk and allocate resources to mitigate hazards during those peak probability shifts.

Using social marketing to increase the use of helmets among bicyclists.

In this study, the authors investigated a social marketing intervention to increase the use of bicycle helmets on a university campus in the southeastern United States. Focus groups of students developed a bicycle helmet program slogan and logo (ie, "The Grateful Head"). The authors trained student bicyclists who already used helmets (n=15) as peer agents. These agents provided bicycle helmet information and asked fellow bicyclists to sign a pledge card to wear a helmet. They gave a coupon for a free helmet to those who pledged to wear a helmet. The authors received a total of 379 pledge cards and distributed 259 helmets. Bicycle helmet use rose from a baseline mean of 27.6% to a mean of 49.3% by the last week of the intervention.

A hierarchical factor analysis of a safety culture survey.

INTRODUCTION: Recent reviews of safety culture measures have revealed a host of potential factors that could make up a safety culture (Flin, Mearns, O'Connor, & Bryden, 2000; Guldenmund, 2000). However, there is still little consensus regarding what the core factors of safety culture are. The purpose of the current research was to determine the core factors, as well as the structure of those factors that make up a safety culture, and establish which factors add meaningful value by factor analyzing a widely used safety culture survey. METHOD: A 92-item survey was constructed by subject matter experts and was administered to 25,574 workers across five multi-national organizations in five different industries. Exploratory and hierarchical confirmatory factor analyses were conducted revealing four second-order factors of a Safety Culture consisting of Management Concern, Personal Responsibility for Safety, Peer Support for Safety, and Safety Management Systems. Additionally, a total of 12 first-order factors were found: three on Management Concern, three on Personal Responsibility, two on Peer Support, and four on Safety Management Systems. RESULTS: The resulting safety culture model addresses gaps in the literature by indentifying the core constructs which make up a safety culture. IMPACT ON INDUSTRY: This clarification of the major factors emerging in the measurement of safety cultures should impact the industry through a more accurate description, measurement, and tracking of safety cultures to reduce loss due to injury.

The additive impact of group and individual publicly displayed feedback: examining individual response patterns and response generalization in a safe-driving occupational intervention.

Additive effects of publicly posting individual feedback following group goal-setting and feedback were evaluated. The turn-signal use of pizza deliverers was studied in a multiple baseline design across two pizza stores. After baseline observations, pizza deliverers voted on a group turn-signal goal and then received 4 weeks of group feedback on their turn-signal use (i.e., group feedback phase). Then, for the next 4 weeks, individual turn-signal use means were publicly posted along with the group feedback (i.e., individual feedback phase). Deliverers at Store A (n = 24) increased their use of turn signals from a mean of 5% during baseline to 16.9% during the group feedback phase and then to 30% during the individual feedback phase. Turn-signal use at Store B (n = 20) increased from 28.9% during baseline to 43.6% during group feedback phase and to 56% during the subsequent individual feedback phase. Individual analyses suggested that deliverers who improved the target behavior during group feedback phase did not increase their turn-signal use further when individual feedback was added. Conversely, most deliverers who did not improve during the group feedback phase increased their turn-signal use when individual feedback was added. Complete intersection stopping increased concurrently with the turn-signal intervention phases from baseline means of 12% and 30% at Store A and B, respectively, to means of 21% and 48% during the interventions.