تاثیر فرهنگ سازمانی بر رضایت شغلی و قصد ترک خدمت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4042||2010||12 صفحه PDF||سفارش دهید||8640 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Sport Management Review, Volume 13, Issue 2, May 2010, Pages 106–117
This investigation examined the impact of organizational culture on job satisfaction and intention to leave the organization through a survey of fitness staff. Organizational culture is commonly known as the values, beliefs and basic assumptions that help guide and coordinate member behaviour. The Cultural Index for Fitness Organizations (CIFO) was developed to measure organizational culture in the fitness industry specifically. Exploratory factor analysis revealed eight factors that represent cultural dimensions common to this context: staff competency, atmosphere, connectedness, formalization, sales, service-equipment, service-programs, and organizational presence. Path analysis was used to examine the relationship among the organizational culture factors, job satisfaction and intention to leave. Results produced a partially mediated model of organizational culture that explained 14.3% of the variance in job satisfaction and 50.3% of the variance with intention to leave the organization. The findings highlight the multidimensionality of organizational culture and its complexity in the fitness industry.
نتیجه گیری انگلیسی
4.1. Description of respondents A total of 612 surveys were distributed and 432 were returned (70.6%). Of the returned surveys, 392 were completed on-site (91%) and 40 were mailed back to the investigators (9%). From the returned surveys 16 did not meet the criteria of current staff from a Canadian fitness club/organization and hence 416 surveys were useable. Of the 416 participants in the study, 327 were women (79%) and 89 were men (21%). The average age of respondents was 33 years (SD = 9.9) with a range of 17–60 years. The average tenure of respondents was 4.5 years (SD = 4.0) with a range of 6 months to 25 years. In terms of job position 156 respondents indicated that they were management (37.5%), 151 were fitness staff (i.e., personal trainers, fitness instructors; 36.3%), 82 were service staff (i.e., customer service, sales; 19.7%), and 27 indicated that they held other jobs within their club/organization (i.e., therapist, nutritionist, data entry; 6.5%). There was some overlap in the reporting of club type with many participants describing their organization according to several criteria. Overall, 77% were from a for-profit facility and 23% were from a not-for-profit club; 69% worked at a coed facility and 31% worked at a women's only fitness club; 66% were from a club that is part of a fitness chain and 34% were from independent fitness organizations. Although data on industry personnel appears to be limited, the profile of participants suggests they were fairly representative of the population of fitness industry staff according to the variables measured. Specifically, labor force data indicate that women comprise 69% of employees in the fitness industry (US Department of Labor, 2007). Similarly, MacIntosh and Doherty (2005) reported that the ratio of women to men staff in the large multi-club private fitness organization they studied was 4:1. They also reported an average age of 30 years for fitness staff in that same organization (MacIntosh & Doherty, 2005). The study sample is comparable to this profile. Industry data is not available regarding the proportion of staff by different positions, however the study sample may be over-represented by those who classified themselves as “management” (37.5% of participants). This was a fairly broad category and fitness clubs/organizations, as with many organizations, are likely to have several levels of management. It may be of value to tease out such data across the industry and in future research. Participants who worked at for-profit clubs appear to be over-represented in the study, comprising 77% of the sample vs. approximately 53% of the population of clubs in the fitness industry (Mintel International Group, 2006). This may be consistent with their representation at the trade show where data were collected however a breakdown of tradeshow attendees was not available. 4.2. CIFO The sample size was determined to be adequate for conducting an exploratory factor analysis based on the Kaiser–Meyer–Olkin sampling statistic (KMO = .94: Tabachnick & Fidell, 2001). A principal components analysis using varimax rotation yielded 11 possible factors which had eigenvalues >1.0. Inspection of the rotated component matrix showed that four items loaded .40 or less while six items correlated within .10 of another factor and hence were removed from the solution. Five items were not conceptually consistent with the emergent factor on which they loaded and were also removed (Nunnally & Bernstein, 1994). This procedure led to the elimination of three factors and resulted in an eight factor solution. As will be discussed shortly, the composition of the organizational culture factors was not completely consistent with the original framework and the factors were subsequently named as follows: (1) staff competency (e.g., staff have credentials, knowledge, positive attitude), (2) atmosphere (e.g., club is welcoming, upbeat, fun), (3) connectedness (e.g., there is sense of affiliation, belonging), (4) formalization (e.g., club has policies, procedures, standards), (5) sales (e.g., sales is emphasized, rewarded), (6) service-equipment (e.g., good variety, quality, availability), (7) service-programs (programs are current, innovative, varied), and (8) organizational presence (e.g., club has long history, positive image). The factor loadings, eigenvalues, and percentages of total variance for each factor are presented in Table 1.Seven of the eight factors demonstrated acceptable levels of internal consistency (Cronbach alpha > .70; Tabachnick & Fidell, 2001). The acceptable core values were: staff competency (n = 9 items, α = .91), atmosphere (n = 7 items, α = .88), connectedness (n = 5 items, α = .86), formalization (n = 5 items, α = .84), sales (n = 4 items, α = .87), service-equipment (n = 3 items, α = .83), and service-programs (n = 3 items, α = .85). The only factor not to meet the criteria was organizational presence (n = 3 items, α = .68). However, since this study was exploratory in nature, and the factor had been identified in previous research (MacIntosh & Doherty, 2008) it was retained and considered with caution in further analyses. In total, 39 of the original 54 items representing the core values remained. Cronbach alpha reliability values were acceptable for job satisfaction (n = 3 items, α = .80) and intention to leave the organization (n = 3 items, α = .81). Next, scale intercorrelations were used to examine the independence of the organizational culture factors (see Table 2). The values ranging from .18 to .78 indicated no problems with multicollinearity (Tabachnick & Fidell, 2001).4.3. Organizational culture, job satisfaction and intention to leave Scale intercorrelations reported in Table 3 confirmed significant relationships between the cultural dimensions and intent to leave as well as the cultural dimensions and job satisfaction. Confirmation of these relationships is necessary to proceed with the testing of a mediated model. The original model examined the effect of the cultural dimensions on job satisfaction, the effect of job satisfaction on intention to leave, and the effect of cultural dimensions on intention to leave. The hypothesized model did not fit the data well: χ2(0) = 0, p < .001; NFI = 1.00; CFI = 1.00; RMSEA = .277. Although the fit indices (NFI, CFI) were acceptable RMSEA was problematic and χ2 was equal to zero. This χ2 value is expected with a fully recursive model where the “estimated parameters perfectly reproduce the sample covariance matrix, [and] χ2 and degree of freedom are equal to zero” (Tabachnick & Fidell, 2001, p. 698). Tabachnick and Fidell further noted that this analysis “is uninteresting because hypotheses about adequacy of the model cannot be tested. However, hypotheses about specific paths in the model can be tested” (p. 698). The results showed several significant and non-significant path coefficients (Table 3) which were the basis for model trimming.A second model based on the elimination of non-significant path coefficients (i.e., staff competencies, sales, service-equipment, organizational presence) from the first model and the re-examination of model fit indices (Kline, 2005), resulted in a partially mediated model that fit the data well: χ2 = 3.57, p = .472; NFI = .99; CFI = 1.00; RMSEA = .033 (see Fig. 2). Three of the cultural dimensions significantly influenced job satisfaction: the first two, atmosphere and service-programs, had a positive association while the third, formalization, had a negative or inverse association with job satisfaction. The dimension of connectedness significantly and inversely influenced intention to leave. Inspection of the structural paths indicated that atmosphere (β = .48, p < .001) and connectedness (β = −.21, p < .001) held moderate to large associations with job satisfaction and intention to leave, respectively, while the association between formalization and job satisfaction (β = −.17, p < .01) was less strong. Service-programs (β = .12, p > .05) was not significantly associated with job satisfaction though it contributed to the overall model fit. Finally, job satisfaction strongly and inversely influenced intention to leave (β = −.80, p < .001). In all, the model explained 14.8% of the variance in job satisfaction and 50.3% of the variance with respect to intention to leave.