Inside-Out-Outside-In: A dual approach process model to developing work happiness

Abstract: This conceptual paper presents the Inside-Out-Outside-In (IO-OI) model, a dual process positive systems science approach to developing work happiness. The model suggests that work happiness of employees is influenced by individual level personal resources developed through positive employee development and positive attitudes (inside-out factors), and social resources developed at the organizational level through positive strategies and positive organizational culture (outside-in factors). The model further specifies three processes that connect outside-in and inside-out factors (attitude re-evaluation, selective exposure and confirmation bias), and a series of feedback loops that support upward spirals of positive development at the individual and organizational levels. We suggest ways in which the IO-OI model can be used and tested. The IO-OI model integrates the fields of attitudes, positive organizational scholarship, and positive organizational behavior, and provides a foundation for understanding how to best foster positive attitudes, create virtuous organizations and foster employee work happiness

In Their Own Words: The Career Stories of Women Leaders in STEM Professions

While STEM professions exhibit rapid growth in both employment opportunity (Carnevale, Smith, & Strohl, 2013) and earnings potential (Talakjowski, 2018), many of these careers - especially those in IT and engineering fields - have traditionally been and still remain male-dominated. For example, the workforces of large technical firms such as Google and Amazon consist of about 30% women with only a few of those women attaining leadership roles (Cheng, 2015). There has been extensive research exploring reasons explaining why women are underrepresented in STEM professions. However, there has been relatively little research investigating reasons why some women not only choose to enter STEM professions but also thrive in their STEM-oriented careers. This study seeks to fill that gap by reporting results from a grounded theory exploration of the career stories of successful women leaders in STEM professions. Our interest in this study is obtaining an understanding of women\u27s career stories from the perspective of the women themselves. We conducted and analyzed semi-structured interviews with 22 women leaders in STEM fields. The interview protocol included questions beginning with family and childhood influences and progressing through adolescence and college choices before examining career progression and aspirations. We also asked about significant individuals who influenced their careers, both positively and negatively, and about their perceptions of both career advantages and disadvantages attributable to gender. While the women included in this study represent a varied group, there are some common themes that are beginning to emerge as we progress with the analysis. Many of the women report strong childhood emphasis on work ethic in their families with some coming from very humble beginnings including both agricultural workers and first-generation immigrants. Most of the respondents reported that they participated in competitive activities in their school years. The women respondents chose to study STEM-related fields both because of an affinity for math, science, and logic and because of the influence of respected family members and mentors. As these women entered their careers, many of them found themselves decidedly in the minority in their male-dominated environments. Two interesting themes are found in the stories of how the women navigated those circumstances. In some cases, the women made deliberate efforts to adapt and fit into those environments; for example, one related an account of how she cultivated an interest in watching sports and playing golf. In other cases, the women credited their ability to build networks and relationships in the workplace as a critical factor in their success. Further discussion of these and other themes as well as the supporting evidence from the interviews will be presented at the conference. Findings from this research will help to develop recommendations both for women interested in STEM professions as well as for organizations that would like to recruit and retain women in their workforce

Mediation Analysis for Censored Survival Data under an Accelerated Failure Time Model

Recent advances in causal mediation analysis have formalized conditions for estimating direct and indirect effects in various contexts. These approaches have been extended to a number of models for survival outcomes including accelerated failure time (AFT) models which are widely used in a broad range of health applications given their intuitive interpretation. In this setting, it has been suggested that under standard assumptions, the “difference” and “product” methods produce equivalent estimates of the indirect effect of exposure on the survival outcome. We formally show that these two methods may produce substantially different estimates in the presence of censoring or truncation, due to a form of model misspecification. Specifically, we establish that while the product method remains valid under standard assumptions in the presence of independent censoring, the difference method can be biased in the presence of such censoring whenever the error distribution of the AFT model fails to be collapsible upon marginalizing over the mediator. This will invariably be the case for most choices of mediator and outcome error distributions. A notable exception arises in case of normal mediator-normal outcome where we show consistency of both difference and product estimators in the presence of independent censoring. These results are confirmed in simulation studies and two data applications

Data Analytics vs. Data Science: A Study of Similarities and Differences in Undergraduate Programs Based on Course Descriptions

The rate at which data is produced and accumulated today is greater than at any point in history with little prospect of slowing. As organizations attempt to collect and analyze this data, there is a tremendous unmet demand for appropriately skilled knowledge workers. In response, universities are developing degree programs in data science and data analytics. As a contribution to the design and development of these programs, this paper presents findings from a review of the descriptions of courses offered in a small sample of undergraduate programs in data science and data analytics. Our investigation clarifies and illustrates the similarities and differences between undergraduate data analytics and data science programs

An applied framework for Positive Education

The increasing momentum of the Positive Psychology movement has seen burgeoning research in positive mental health and adaptive functioning; a critical question is how this knowledge can now be applied in real-world settings. Positive Education seeks to combine principles of Positive Psychology with best-practice teaching and with educational paradigms to promote optimal development and flourishing in the school setting. Interest in Positive Education continues to grow in line with increasing recognition of the important role played by schools in fostering wellbeing, and the link between wellbeing and academic success. To date, however, a framework to guide the implementation of Positive Education in schools has been lacking. This paper provides an overview of the Geelong Grammar School (GGS) Model for Positive Education, an applied framework developed over five years of implementing Positive Education as a whole-school approach in one Australian school. Explicit and implicit teaching in combination with school-wide practices target six wellbeing domains, including positive emotions, positive engagement, positive accomplishment, positive purpose, positive relationships, and positive health, underpinned by a focus on character strengths. The Model provides a structured pathway for implementing Positive Education in schools, a framework to guide evaluation and research, and a foundation for further theoretical discussion and development

Mathematical modelling to support blood collection for the Welsh Blood Service

Human blood is a scarce resource and its role in healthcare is fundamental, with donated blood saving the lives of many on a daily basis. The blood supply chain is responsible for the transfer of blood from donor to the recipient, but the availability of such an invaluable resource as human blood is ultimately attributable to the many voluntary donors. Thus, the efficiency of the collection of donated blood is crucial to the downstream effectiveness of the blood supply chain. Working in partnership with the Welsh Blood Service, our aim is to create a decision support tool to aid the scheduling process to match supply and demand of blood products, whilst minimising costs and wastage in the system. We present an integer linear programme model that consists of two stages. The first stage schedules mobile blood donation clinics, considering over 300 locations, with the objective to minimise both the number of clinics scheduled within the planning horizon and the amount of blood collected that exceeds the demand. The second stage assigns workers to each scheduled clinic, with the objective of minimising costs such as overtime costs. Both stages of this scheduling model are developed in Python and are solved using PuLP - an open source Python package which utilises COIN-OR CBC solver. Test instances are designed and the experimental results are presented which demonstrate the effectiveness of the two-stage model to improve cost and time efficiencies of the collections process at the Welsh Blood Service, in addition to enabling the matching of supply to demand. Finally, some insights regarding the staffing levels of each region are discusse