Table 3

Rescaled part-worthsa,b of attribute levels for the archetypical preference classes

Career-oriented type (n = 61) auc p = 0.95Job-oriented type (n = 171) auc p = 0.95Totala (n = 232)
AttributesLevelsRescaledb part-worth estimatesRelative importancedRankRescaledb part-worth estimatesRelative importancedRankRescaledb part-worth estimatesRelative importancedRank
Company description and job design(1) competitive company and process-oriented employee for varied tasks31.8220.1471.4445.3280.0028.22
(2) client-oriented company and expert for demanding responsibilities40.68  29.40  32.95  
(3) company with tradition and team member with good demeanor9.11  0.36  0  
Salary and bonus(1) job tenure and company performance69.2331.9120.6336.3323.6618.43
(2) qualification and clients consulted38.60  1.99  0  
(3) fringe benefits and team performance19.19  58.83  52.08  
Work climate(1) structured work and strong corporate culture48.9520.6321.6658.5125.9235.51
(2) self-determined work and enthusiasm29.11  93.93  100.86  
(3) work-life balance and friendly climate16.72  2.30  0  
Training and development(1) in-house training and in-house career18.6227.4255.2933.3450.8717.94
(2) professional training and expertise17.53  41.12  33.65  
(3) on-the-job training and personal development60.43  3.05  0  

Note(s): a Based on the 232 respondents remaining after elimination of seven respondents due to missing values and implausible answers in the socio-demographic variables

b Arithmetic mean of rescaled part-worths per cluster. The part-worth estimates are rescaled so that the sum of the part-worth values across the three attributes for each respondent equals 400. It does not affect the magnitude of any part-worth, but provides a common scale across all part-worth values for comparison across attributes and respondents

c Approximately unbiased p-values over 95% indicate that the cluster are strongly supported by the data

d Arithmetic mean of relative importance of each attribute per cluster. The relative importance of each attribute reflects how much a job attribute influences the choice of a job profile. Importance weights are calculated by computing the difference between the largest and the smallest part-worth for each attribute, summing the differences, and normalizing to 100. Attribute importance scores sum up to 100 across all three attributes for each respondent

Model Fit: likelihood ratio statistic = 1,494.67, df = 10, p ≤ 0.001; McFadden pseudo R2 = 0.472

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