Management control systems and performance: Evidence from the Greek shipping industry

Maritime Policy and Management

Volumn 37, Issue 6, 2010, Pages 625-660

  Triantafylli, Androniki A ^a   Ballas, Apostolos A ^b  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL SYSTEM; IMPLEMENTATION PROCESS; SHIPPING; TRANSPORTATION ECONOMICS;

GREECE;

EID: 77958610283     PISSN: 03088839     EISSN: 14645254     Source Type: Journal    
DOI: 10.1080/03088839.2010.514957     Document Type: Article

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81. The sample does not include shipping companies listed on a stock exchange.
82. Factors include the extent to which MCS were used to: Achieve tight control and monitoring over procedures, rules/standards. Achieve learning and flexibility to act upon information. Minimize risks. Increase revenue. Minimize costs and achieve operating efficiencies. Collect internally oriented information. Collect externally oriented information. Financial performance measures. Non-financial performance measures.
83. Since the factors described by the interviewees were observed as emphases and were not mutually exclusive, factors are allowed to be correlated. Principal Axis Factoring is used and axes are rotated using Promax so that factors are not constrained to be orthogonal to each other.
84. "Since the factors described by the interviewees were observed as emphases and were not mutually exclusive, factors are allowed to be correlated. Principal Axis Factoring is used and axis are rotated using Promax-an oblique rotation technique-so that factors are not constrained to be orthogonal to each other" [87]. The Kaiser-Meyer-Olkin measure of sampling adequacy equals 0.793 and this suggests a factor solution that is appropriate. The adequacy of solution is supported by the Bartlett's test of Sphericity which equals 366.283 with a p-value of 0.000. The corresponding Cronbach alpha measure of reliability is 0.868.
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89. In statistics, correlation is any of a broad class of statistical relationships between two or more random variables or observed data values. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. Correlations can also suggest possible causal or mechanistic relationships. The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient, or "Pearson's correlation." It is obtained by dividing the covariance of the two variables by the product of their SD. The Pearson correlation is {thorn}1 in the case of a perfect positive (increasing) linear relationship, 1 in the case of a perfect decreasing (negative) linear relationship, and some value between 1 and 1 in all other cases, indicating the degree of linear dependence between the variables. As it approaches zero, there is less of a relationship. The closer the coefficient is to either 1 or 1, the stronger the correlation between the variables. If the variables are independent, Pearson's correlation coefficient is 0, but the converse is not true because the correlation coefficient detects only linear dependencies between two variables.
90. Note, that, as a result of our categorization criteria, 3 of the 29 MCS were not assigned to any of the three types of MCS. This should not be viewed as a limitation of the analysis, since our objective was not to categorize any MCS implemented by managers of the shipping companies, but to provide a general, intuitive framework that would capture the types of MCS most often introduced by shipping companies with different information needs. With respect to the three unassigned MCS, no convincing evidence was found of a systematic relation between their introduction among the set of MCS and the firm's informational needs. For example, marketing databases, suppliers databases, and benchmarking of the competitors was not classified as basic MCS because shipping companies explained that since the freight rates are a result of demand for vessels by charterers and supply of vessels by shipowners and of the international freight market, it is of little interest the implementation of such MCS in a shipping company. Thus, we conclude that the nature of implementation of these two MCS was not consistent with the notion of Basic MCS that was pursuing in the survey-i.e., MCS commonly adopted because they are essential to the development of a shipping company. Certainly, the categorization criteria are partly subjective and the resulting classification should be viewed as tentative.
91. Planned Maintenance System (PMS) is an electronic system, usually on line with the vessels, which monitors the requisitions for spare parts etc. from vessels.
92. The robustness test of the results that link each dimension with specific implemented MCS was verified by analyzing the sub-sample of firms owning only bulk carriers. The list of MCS assigned to each category remains unchanged except that Budget controls and Capital Budgeting controls appear to be Basic MCS.
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94. Logistic Regression is a form of regression that allows the prediction of discrete variables (only) by a mix of continuous and/or categorical or discrete predictors. A binomial logit model is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but it is suited to models where the dependent variable is dichotomous. In this case, the dependent variable is dichotomous (MCS are described as a categorical variable, CHOICEMCS that describes two categories of MCS, Cost MCS, and External Information MCS). "A binomial logit model is useful for situations where one wants to be able to predict the presence or absence of a characteristic or outcome on the basis of the values of a set of predictor variables. It is similar to a linear regression model but it is suited to models where the dependent variable is dichotomous. In this case, it is appropriate because the dependent variable (CHOICEMCS) is dichotomous taking the value 0 (or, 1) for Cost MCS and 1 for External Information MCS".
95. While the categories of MCS are not mutually exclusive (shipping companies may emphasize all of them to some extent), turning relative emphases into "choices" allows to control for biases in terms or ratings clustering (differential respondent's propensity for rating levels, e.g., someone giving all 5's vs. someone giving all 1's) and in terms of ratings dispersion (differential respondents' propensity for ratings variation, e.g., someone answering in a range 1-7 s vs. someone answering in a range 3-4 s). Moreover, to the extent that a higher emphasis on one set of MCS reflects a greater use of the firm's limited resources (financial, human, etc.), emphases can be viewed also as "choices".
96. Mintzberg [82] draws a crucial distinction between intended strategy and realized strategy. Realized strategies emerge through events and environmental interactions as they unfold over time. Thus, intended strategies may not be realized because they are based on erroneous or incomplete information, environmental circumstances may change or they may simply not be enacted [38]. Similarly, realized strategies may emerge unintentionally.
97. DECENTRALIZATION is a composite measure developed through principal components describing the extent of decentralization of the firm. It explains approximately 64% of the variation found in four questions in the survey that ask about the extent to which shipping company's personnel has authority to make decisions about: (1) long-lived assets; (2) invoices for dry docking; (3) sales and purchases; and (4) voyage fixture confirmation. Higher values indicate higher levels of decentralization (decision making by managers rather than senior management. The corresponding Cronbach alpha is 0.664. In order to associate higher values to decentralization, the scale of question was flipped by calculating 6 minus the number indicated by the respondent.
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105. To complement the analysis in the multinomial model and test the robustness of its results, we also conducted two binomial logistic regressions, where the dependent variable reflects the relative emphasis on each of the two MCS (Low Cost or External Information). For the independent variables, we interacted each of the strategy variables (LOWCOST and DIFFERENTIATION) with the variable 1-SEARCHSTRAT and thus we redefined LOWCOST as LOWCOSTrobust 1/4 LOWCOST (1-SEARCHSTRAT) and DIFFERENTIATION as DIFFERENTIATIONrobust 1/4 DIFFERENTIATION (1-SEARCHSTRAT). This means that strategy variables are set to zero in the cases when SEARCHSTRAT 1/41 (30% of the observations). If the shipping company has optimally chosen its MCS based on its strategy, but the strategy is not the "right" one, this would lead to a negative relation between strategy measures and MCS and by interacting strategy measures with 1-SEARCHSTRAT biasing against finding the hypothesized relationship is prevented. The results are generally consistent with the findings in the multinomial model.
106. Pearson correlations among variables report that Differentiation and Low Cost strategies are negatively correlated consistently with Porter [95]. Shipping companies pursuing Low Cost strategies place more emphasis on Cost MCS (consistent with H1a) and less emphasis on external information MCS. Shipping companies pursuing Differentiation Strategies put more emphasis on External Information MCS and less emphasis on Cost MCS although this result seems to be not statistically significant. Not surprisingly, subsidiaries tend to follow differentation strategies and they have a positive correlation with strategy search, perhaps reflecting the fact that subsidiaries are still defining their specific differentiation strategy. The impact regulations have on the adoption and implementation of MCS is positively correlated with decentralized firms and shipping companies pursuing differentiation strategies. Differentiators also tend to adopt vertical diversification as a way to diversify their activities. Moreover, shipping companies adopting External Information MCS tend to be in the process of defining their strategy (positive correlation between External Information MCS and strategy search).
107. In each column, the constraint is imposed that the coefficients related to outcome "B" be zero, given that the binomial model is not identified without imposing this criterion [110, p. 153].
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109. In panel A, results indicate that for a unit change in the "differentiation strategy" measure, the log-odd ratio of COST MCS versus EXTERNALINFO MCS is expected to increase 106 times [where 1.061/4exp0.059].
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111. When using multiple measure items for a construct, it is important to analyze the validity of the measures. This study examines validity in several different manners and is concerned with both content validity and construct validity. Content validity can be assessed by (1) the "plan and procedures of construction"; (2) appearance or face validity; and (3) a measure of internal consistency through an empirical measure of reliability [114]. Construct validity can be assessed by (1) specifying an appropriate domain of observables underlying the construct; (2) using factor analysis to find relationships among the observables; and (3) using correlation analysis to find relationships among the constructs [114]. To satisfy these requirements several steps were taken. First, the existing literature was thoroughly reviewed to establish appropriate domains. Second, the variables tested use previously validated questions from [50]. Third, multiple site visits with shipping companies were conducted with the purpose of gaining more knowledge about the domain being studied. Fourth, the construction of the survey question follows the guidelines set forth by
112. Dillman [115]. Fifth, pretest respondents reviewed the questions for face validity. Sixth, empirical tests suggested by Nunnally [114] for both content and construct validity were conducted on the survey responses. The empirical tests for content validity include (1) reviewing the range of responses; and (2) calculating Cronbach's alpha which is a statistical measure of internal reliability, and for construct validity include (1) factor and (2) correlation analysis. For the variable LOWCOST the Cronbach's Alpha is equal to 0.905, whereas for the variable DIFFERENTIATIATION is equal to 0.765 and for the variable DECENTRALIZATION equals 0.864. For the variable USEFULMCS, the Cronbach's alpha equals 0.855 indicating an acceptable level of internal validity. Factor analyses for this measure reveals that the measure USEFULMCS is unidimensional (since only one factor has eigenvalues41) with explained variance of 67%. All variables are statistically positively associated. For the variable LOWCOST the Cronbach's Alpha is equal to 0.905, whereas for the variable DIFFERENTIATIATION is equal to 0.765 and for the variable DECENTRALIZATION equals 0.864.
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115. The questions that comprise the composite measure USEFULMCS are the following: 1. Control systems facilitated my company's growth. 2. Control reports have provided timely information for managers to respond to new threats and opportunities. 3. Control systems have protected my company from loss or excessive risk. 4. Control systems have reduced flexibility (RC). 5. Control systems provide information that is NOT useful to management (RC). 6. Control systems monitor virtually all tasks in the organization. 7. There is frequent reporting control information to senior managers. In the survey, question 1 indicates most useful and 5 least useful. For questions that are reverse coded (RC) in order to flip the scale, we calculated them as 6 minus the rating given in answering the question.
116. Linear Regression Models (LRM) with ordinal outcomes-such as Likert ratings-can provide misleading results, since LRM assume that the intervals between adjacent categories are equal. Long [110] suggests the use of models specifically designed for ordinal variables such as the Ordinal Logit Model. 118. Using OLS assumes that the intervals between Likert ratings are equal, i.e., that the difference between the Likert ratings of 1 and 2 is the same as the difference between Likert ratings of 2 and 3. This assumption is clearly violated in the case of PERCPERFORM, where each rating (1,2, 3, etc.) was associated to a precise economic definition (bottom 10%performance, bottom 25% performance, medium performance, etc.) However, it is a more reasonable assumption for USEFULMCS, where respondents were simply asked to choose between five numbers placed between two opposite ends (strongly agree to strongly disagree).
117. EXPERIENCE: Dummy indicating whether the CEO (or whoever was in charge of introducing and/or implementing MCS) had previous experience in introducing and/or implementing controls in shipping companies. AGE: Number of years since the date of founding. SIZE: Number of vessels owned by the shipping company.
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126. Note that if the shipping company has optimally chosen its MCS based on its strategy, but the strategy is not the "right" one, this would lead to a negative relation between the FIT measure and performance, thereby biasing against finding the hypothesized positive relation. To assess this problem, we interact a dummy that proxies for "right" strategy with the variable FIT. This dummy RIGHTSTRAT equals 0 if the firm changed its strategy-an indication of "wrong" strategy in the past, and 1 otherwise. Results remain the same for all variables.
127. Note that the performance effect presented in the analysis cannot be exclusively attributed to the fit between the MCS and the strategy. This is captured by the multinomial model which includes other organizational variables separate from strategy. Two additional analyses were conducted to better understand whether the fit between MCS and strategy plays an important role on performance: Results of Table 6, were replicated for the sub-sample of firms with a low cost score above median, given that low cost strategy was the only strategy variable that was a significant predictor in the multinomial model Results in Table 6, were replicated using a re-defined FIT variable, where the multinomial model is substituted for the one that includes only the strategy variables as exploratory variables. The FIT variable is positively associated with the perceived performance and usefulness of MCS in both tests.