Relationship of organizational culture, teamwork and job satisfaction in interprofessional teams

BACKGROUND: Team effectiveness is often explained on the basis of input-process-output (IPO) models. According to these models a relationship between organizational culture (input = I), interprofessional teamwork (process = P) and job satisfaction (output = O) is postulated. The aim of this study was to examine the relationship between these three aspects using structural analysis. METHODS: A multi-center cross-sectional study with a survey of 272 employees was conducted in fifteen rehabilitation clinics with different indication fields in Germany. Structural equation modeling (SEM) was carried out using AMOS software version 20.0 (maximum-likelihood method). RESULTS: Of 661 questionnaires sent out to members of the health care teams in the medical rehabilitation clinics, 275 were returned (41.6 %). Three questionnaires were excluded (missing data greater than 30 %), yielding a total of 272 employees that could be analyzed. The confirmatory models were supported by the data. The results showed that 35 % of job satisfaction is predicted by a structural equation model that includes both organizational culture and teamwork. The comparison of this predictive IPO model (organizational culture (I), interprofessional teamwork (P), job satisfaction (O)) and the predictive IO model (organizational culture (I), job satisfaction (O)) showed that the effect of organizational culture is completely mediated by interprofessional teamwork. The global fit indices are a little better for the IO model (TLI: .967, CFI: .972, RMSEA .052) than for the IPO model (TLI: .934, CFI: .943, RMSEA: .61), but the prediction of job satisfaction is better in the IPO model (R(2) = 35 %) than in the IO model (R(2) = 24 %). CONCLUSIONS: Our study results underpin the importance of interprofessional teamwork in health care organizations. To enhance interprofessional teamwork, team interventions can be recommended and should be supported. Further studies investigating the organizational culture and its impact on interprofessional teamwork and team effectiveness in health care are important. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-015-0888-y) contains supplementary material, which is available to authorized users

An omnichannel approach to retailing: demystifying and identifying the factors influencing an omnichannel experience

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The purpose of this research is to identify the factors that influence an omnichannel experience. Omnichannel is an emerging approach to retailing that responds to the changing nature of how customers shop in alternation between online and offline shops, and the increasing use of digital devices (e.g. smartphones and tablets) retailers are focusing and establishing a seamless integrated approach to their services. Omnichannel is now a hot topic in retailing but there is a lack of empirical studies into the factors that influence an omnichannel experience. Using a mixed methods approach, we propose and empirically test a conceptual model that identifies four factors influencing an omnichannel experience: brand familiarity; customisation; perceived value, and technology readiness. We conceptualise omnichannel to include three key channels; in-store, online and mobile. 246 questionnaires were collected and analysed using PLS-SEM and 11 interviews with marketing/omnichannel professionals. Our results indicate that brand familiarity has a strong influence on omnichannel (in-store, online and mobile) while perceived value has a negative impact on mobile experience. Our results show that retailers need to consider multiple factors, such as brand familiarity customisation, perceived value and technology readiness as influencing factors of an omnichannel experience, and plan the use of multiple touchpoints simultaneously to enhance their overall customer’s experience. Although this study demonstrates the significant factors influencing an omnichannel experience, questions remain regarding the exact use of each touchpoint by customers and the extent of overlap between the touchpoints. Our research attempts to address the lack of academic research on what factors influence an omnichannel experience

Stretched Polymers in a Poor Solvent

Stretched polymers with attractive interaction are studied in two and three dimensions. They are described by biased self-avoiding random walks with nearest neighbour attraction. The bias corresponds to opposite forces applied to the first and last monomers. We show that both in $d=2$ and $d=3$ a phase transition occurs as this force is increased beyond a critical value, where the polymer changes from a collapsed globule to a stretched configuration. This transition is second order in $d=2$ and first order in $d=3$. For $d=2$ we predict the transition point quantitatively from properties of the unstretched polymer. This is not possible in $d=3$, but even there we can estimate the transition point precisely, and we can study the scaling at temperatures slightly below the collapse temperature of the unstretched polymer. We find very large finite size corrections which would make very difficult the estimate of the transition point from straightforward simulations.Comment: 10 pages, 16 figure

Web-based alcohol intervention:study of systematic attrition of heavy drinkers

Background: Web-based alcohol interventions are a promising way to reduce alcohol consumption because of their anonymity and the possibility of reaching a high numbers of individuals including heavy drinkers. However, Web-based interventions are often characterized by high rates of attrition. To date, very few studies have investigated whether individuals with higher alcohol consumption show higher attrition rates in Web-based alcohol interventions as compared with individuals with lower alcohol consumption. Objectives: The aim of this study was to examine the attrition rate and predictors of attrition in a Web-based intervention study on alcohol consumption. Methods: The analysis of the predictors of attrition rate was performed on data collected in a Web-based randomized control trial. Data collection took place at the University of Konstanz, Germany. A total of 898 people, which consisted of 46.8% males (420/898) and 53.2% females (478/898) with a mean age of 23.57 years (SD 5.19), initially volunteered to participate in a Web-based intervention study to reduce alcohol consumption. Out of the sample, 86.9% (781/898) were students. Participants were classified as non-completers (439/898, 48.9%) if they did not complete the Web-based intervention. Potential predictors of attrition were self-reported: alcohol consumption in the last seven days, per week, from Monday to Thursday, on weekends, excessive drinking behavior measured with the Alcohol Use Disorder Identification Test (AUDIT), and drinking motives measured by the Drinking Motive Questionnaire (DMQ-R SF). Results: Significant differences between completers and non-completers emerged regarding alcohol consumption in the last seven days (B=-.02, P=.05, 95% CI [0.97-1.00]), on weekends (B=-.05, P=.003, 95% CI [0.92-0.98]), the AUDIT (B=-.06, P=.007, 95% CI [0.90-0.98], and the status as a student (B=.72, P=.001, 95% CI [1.35-3.11]). Most importantly, non-completers had a significantly higher alcohol consumption compared with completers. Conclusions: Hazardous alcohol consumption appears to be a key factor of the dropout rate in a Web-based alcohol intervention study. Thus, it is important to develop strategies to keep participants who are at high risk in Web-based interventions

A Comparison of Four Probability-Based Online and Mixed-Mode Panels in Europe

Inferential statistics teach us that we need a random probability sample to infer from a sample to the general population. In online survey research, however, volunteer access panels, in which respondents self-select themselves into the sample, dominate the landscape. Such panels are attractive due to their low costs. Nevertheless, recent years have seen increasing numbers of debates about the quality, in particular about errors in the representativeness and measurement, of such panels. In this article, we describe four probability-based online and mixed-mode panels for the general population, namely, the Longitudinal Internet Studies for the Social Sciences (LISS) Panel in the Netherlands, the German Internet Panel (GIP) and the GESIS Panel in Germany, and the Longitudinal Study by Internet for the Social Sciences (ELIPSS) Panel in France. We compare them in terms of sampling strategies, offline recruitment procedures, and panel characteristics. Our aim is to provide an overview to the scientific community of the availability of such data sources to demonstrate the potential strategies for recruiting and maintaining probability-based online panels to practitioners and to direct analysts of the comparative data collected across these panels to methodological differences that may affect comparative estimates