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adoption, then the question When was TQM in systems development formally initiated in your organization may not capture relative earliness The term relative needs a benchmark for comparison Relative measurements are common in multicriteria decision making models, where pairwise comparisons are used One example of such a model is used in the analytic hierarchy process (AHP) The way the question is currently phrased, it only captures the perceived time since the formal adoption of TQM in the organization The measure, thus, is an absolute measure Furthermore, if the unit of analysis is the information systems department (ISD), the question When was TQM in systems development formally initiated in your organization appears to shift the unit of analysis from ISD to an organization This leads to a dangling unit of analysis The second problem is that of the scale design For example, categories on the scales are not mutually exclusive If an organization has formally adopted TQM practices for 2 years, a respondent may choose one of the 1 2 year or 2 3 year options as 2 years appears in both intervals Thus the measurement scale appears to be overlapping We believe that relative earliness of adoption can only be measured on either a ratio scale or a pairwise comparison interval scale The measurement of the dependent variable on either a ratio or a pairwise comparison interval scale is rare in IS surveys, however The author used Cox regression analysis for testing his hypotheses related to adoption swiftness A justi cation for Cox regression and survival analysis was given by citing a reference in the literature We believe that Cox regression analysis is not appropriate for the current research for the following two reasons: 1 Survey research is not longitudinal research 2 Regression coef cients from Cox regression cannot be used to test the proposed hypotheses A description of Cox regression is as follows Let Ni(t) number of failures for subject i at or before time t Yi(t) 1 if subject is under observation, and 0 otherwise Zi(t) vector of time-dependent covariates for each individual Li t cumulative hazard function, which gives the expected number of events for the individual by time t The Cox (proportional hazards) regression model can be written as follows: dLi t dL0 t eb Zi t Yi t Given several events over time t, the above model is tted by estimating the regression coef cients bs and baseline cumulative hazard L0 t , so that the following

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partial likelihood is maximized for the product over all event times t of Pn eb Zi t b0 Zj t j 1 Yj t e

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where the individual i is the one failing at time t The b coef cients are called the odds ratios, which express the increase of hazard in the presence of a covariate Zi(t) In the presence of longitudinal data, Cox regression is an appropriate methodology to use Cox regression is a popular approach for survival analysis of patients suffering from a disease and survival of mutual funds or when using Cox regression on medical data The time-dependent covariates model examines the in uence of patients characteristics, which change over time, on the survival rates Since the respondents responded to the survey only once, no data was available across time and Cox regression analysis is therefore inappropriate The problem is further aggravated when the signi cance of Cox regression coef cients is used to test the hypotheses Cox regression coef cients provide information on the impact of a covariate in increasing the hazard (expected number of events), which is the dependent variable in the model Using the signi cance results for testing hypotheses other than hazard increase is therefore inappropriate The author uses these coef cients to test the level of signi cance of the impact of independent variables on the dependent variable (swiftness) Thus we believe that the set of hypotheses related to swiftness are wrongly tested We believe that the reference cited for justifying survival analysis may suffer from similar problems as well Intensity of Adoption of TQM in Systems Development The author offers three de nitions for intensity of TQM adoption The rst one is the number of these practices that have been adopted The second de nition is intensity, which measures the relative completeness of adoption And the third de nition is proportion of these practices that have been adopted Based on the formula shown, the measurement of intensity appears to be an absolute measure and not a relative measure In either case, the content validity problems from the rst dependent variable apply to the second dependent variable as well The intensity of adoption is operationalized as percentage of responses that take a value of over 4 for 10 questions (measured on 7-point Likert scale) Unnoticed by the author, the formula used by the author assigns respondents to one of the 11 mutually exclusive and collective exhaustive categories These categories are intensity of adoption taking one value from the set A {0, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10} The creation of 11 categories transforms the interval scale to a categorical scale with some loss of information Furthermore, the scale transformation changes the distribution of the dependent variable to the binomial distribution In this section, we analytically illustrate the change in distribution and provide analytic models to estimate the mean and variance of the binomial distribution Let B {p0, p1, , p10} denote the set of relative frequencies of 116 usable responses taking the values in the set A According to the author, there were a total

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