Corporate branding’s influence on front-line employee and consumer value co-creation in UK household consumer markets

General managers are presented with an extensive opportunity to innovate and gain market advantage from front-line employees (FLEs) and consumers working together to exchange services and co-create value. To do this, general managers need to understand more about what influences the content and quality of FLE and consumer service exchanges? What predisposes FLEs to commit to service exchange and value co-creation? And what organizational phenomena can general managers use to influence this predisposition? This article presents results from an empirical research study of FLEs employed by a firm that provides installation, servicing and emergency services to domestic households across the United Kingdom. The study reveals the importance of the firm’s corporate brand in its influence upon FLE’s sense of membershipand attachment to a firm (organizational identity) and the consequent effect of this on their predisposition for serviceexchange (organizational commitment), that is, whether FLEs want to remain in their role, because they feel they ought to,want to or they have too much to lose by leaving

Assessment of Survivor Concerns (ASC): A newly proposed brief questionnaire

BACKGROUND: The purpose of this study was to design a brief questionnaire to measure fears about recurrence and health in cancer survivors. Research involving fear of recurrence has been increasing, indicating that it is an important concern among cancer survivors. METHODS: We developed and tested a six-item instrument, the Assessment of Survivor Concerns (ASC). Construct validity was examined in a multiple group confirmatory factor analysis (CFA) with 592 short-term and 161 long-term cancer survivors. Convergent and discriminant validity was examined through comparisons with the PANAS (Positive and Negative Affect Schedule) and the CES-D (Center for Epidemiologic Studies Depression) measures. RESULTS: CFA models for the ASC with short- and long-term survivors showed good fit, with equivalent structure across both groups of cancer survivors. Convergent and discriminant validity was also supported through analyses of the PANAS and CES-D. One item (children's health worry) did not perform as well as the others, so the models were re-run with the item excluded, and the overall fit was improved. CONCLUSION: The ASC showed excellent internal consistency and validity. We recommend the revised five-item instrument as an appropriate measure for assessment of cancer survivor worries

Multiple dimensions of health locus of control in a representative population sample: ordinal factor analysis and cross-validation of an existing three and a new four factor model

<p>Abstract</p> <p>Background</p> <p>Based on the general approach of locus of control, health locus of control (HLOC) concerns control-beliefs due to illness, sickness and health. HLOC research results provide an improved understanding of health related behaviour and patients' compliance in medical care. HLOC research distinguishes between beliefs due to Internality, Externality powerful Others (POs) and Externality Chance. However, evidences for differentiating the POs dimension were found. Previous factor analyses used selected and predominantly clinical samples, while non-clinical studies are rare. The present study is the first analysis of the HLOC structure based on a large representative general population sample providing important information for non-clinical research and public health care.</p> <p>Methods</p> <p>The standardised German questionnaire which assesses HLOC was used in a representative adult general population sample for a region in Northern Germany (N = 4,075). Data analyses used ordinal factor analyses in LISREL and Mplus. Alternative theory-driven models with one to four latent variables were compared using confirmatory factor analysis. Fit indices, chi-square difference tests, residuals and factor loadings were considered for model comparison. Exploratory factor analysis was used for further model development. Results were cross-validated splitting the total sample randomly and using the cross-validation index.</p> <p>Results</p> <p>A model with four latent variables (Internality, Formal Help, Informal Help and Chance) best represented the HLOC construct (three-dimensional model: normed chi-square = 9.55; RMSEA = 0.066; CFI = 0.931; SRMR = 0.075; four-dimensional model: normed chi-square = 8.65; RMSEA = 0.062; CFI = 0.940; SRMR = 0.071; chi-square difference test: p < 0.001). After excluding one item, the superiority of the four- over the three-dimensional HLOC construct became very obvious (three-dimensional model: normed chi-square = 7.74; RMSEA = 0.059; CFI = 0.950; SRMR = 0.079; four-dimensional model: normed chi-square = 5.75; RMSEA = 0.049; CFI = 0.965; SRMR = 0.065; chi-square difference test: p < 0.001). Results were confirmed by cross-validation. Results based on our large community sample indicated that western general populations separate health-related control-beliefs concerning formal and informal assistance.</p> <p>Conclusions</p> <p>Future non-clinical HLOC studies in western cultures should consider four dimensions of HLOC: Internality, Formal Help, Informal Help and Chance. However, the standardised German instrument needs modification. Therefore, confirmation of our results may be useful. Future research should compare HLOC structure between clinical and non-clinical samples as well as cross-culturally.</p

From social context and resilience to performance through job satisfaction: A multilevel study over time

Giving the crucial role of organizational context in shaping individual attitudes and behaviors at work, in this research we studied the effects of collective work-unit Perceptions of Social Context (PoSC) on individual work resilience and two key individual outcomes: job satisfaction and job performance as rated by the supervisor. We theorized that collective PoSC act as antecedents of individual variables, and that individual job satisfaction mediates the relationship between collective PoSC and job performance, and between work resilience and job performance over time. A sample of 305 white-collar employees, clustered in 67 work-units, participated in the study. Hierarchical linear modeling highlighted that collective PoSC are significant related to individual work resilience. Moreover, results showed that individual job satisfaction fully mediates the relationship between collective PoSC and individual job performance and the relationship between individual work resilience and individual job performance. At a practical level, results suggest that interventions on collective PoSC may increase work resilience, job satisfaction and job performance over time at the individual level

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Social network size, loneliness, physical functioning and depressive symptoms among older adults: Examining reciprocal associations in four waves of the Longitudinal Aging Study Amsterdam (LASA)

Previous research indicates that social isolation, loneliness, physical dysfunction and depressive symptoms are interrelated factors, little is known about the potential pathways among them. The aim of the study is to analyse simultaneously reciprocal relationships that could exist between the four factors to clarify potential mediation effects. METHODS Within a large representative sample of older people in the Longitudinal Aging Study Amsterdam (LASA), participants aged 75 and over were followed up over a period of 11 years (four waves). We tested cross-lagged and autoregressive longitudinal associations of social network size, loneliness, physical functioning and depressive symptoms using structural equation modelling (SEM). RESULTS Several statistically significant cross-lagged associations were found: decreasing physical functioning (Coef.=-0.03; p<0.05), as well as social network size (Coef.=-0.02; p<0.05), predicted higher levels of loneliness, which predicted an increase in depressive symptoms (Coef.=0.17; p<0.05) and further reduction of social network (Coef.=-0.20; p<0.05). Decreasing physical functioning also predicted an increase in depressive symptoms (Coef.=-0.08; p<0.05). All autoregressive associations were statistically significant. CONCLUSION Interventions focused on promoting social activities among older adults after negative life events, such as loss of social contacts or declining physical function, may alleviate feelings of loneliness and act as mental health protector

Structural equation and log-linear modeling: a comparison of methods in the analysis of a study on caregivers' health

BACKGROUND: In this paper we compare the results in an analysis of determinants of caregivers' health derived from two approaches, a structural equation model and a log-linear model, using the same data set. METHODS: The data were collected from a cross-sectional population-based sample of 468 families in Ontario, Canada who had a child with cerebral palsy (CP). The self-completed questionnaires and the home-based interviews used in this study included scales reflecting socio-economic status, child and caregiver characteristics, and the physical and psychological well-being of the caregivers. Both analytic models were used to evaluate the relationships between child behaviour, caregiving demands, coping factors, and the well-being of primary caregivers of children with CP. RESULTS: The results were compared, together with an assessment of the positive and negative aspects of each approach, including their practical and conceptual implications. CONCLUSION: No important differences were found in the substantive conclusions of the two analyses. The broad confirmation of the Structural Equation Modeling (SEM) results by the Log-linear Modeling (LLM) provided some reassurance that the SEM had been adequately specified, and that it broadly fitted the data

A framework for power analysis using a structural equation modelling procedure

BACKGROUND: This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. METHODS: The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. This represents the hypothesised population effects. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. CONCLUSIONS: The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres

Coping with loneliness: What do older adults suggest?

Objectives: A limited amount of information is available on how older adults cope with loneliness. Two ways of coping are distinguished here, i.e. active coping by improving relationships and regulative coping by lowering expectations about relationships. We explore how often older adults suggest these options to their lonely peers in various situations and to what extent individual resources influence their suggestions. Method: After introducing them to four vignettes of lonely individuals, discriminating with regard to age, partner status, and health, 1187 respondents aged 62 to 100 from the Longitudinal Aging Study Amsterdam were asked whether this loneliness can be alleviated by using various ways of coping. Results: In general, both ways of coping were often suggested. However, regression analyses revealed that active coping was suggested less often to people who are older, in poor health, or lonely and by older adults who were employed in midlife and have high self-esteem. Regulative coping was suggested more often to people who are older and by older adults with a low educational level and with low mastery. Conclusions: Coping with loneliness by actively removing the stressor is less often seen as an option for and by the people who could benefit most from it. This underlines the difficulty of combating loneliness