Assessing the structural change of strategic mobility : determinants under hypercompetitive environments

Documents de treball Departament d'economia de l'empresa ASSESSING THE STRUCTURAL CHANGE OF STRATEGIC MOBILITY DETERMINANTS UNDER HYPERCOMPETITIVE ENVIRONMENTS José Ángel Zúñiga Vicente José David Vicente Lorente Document de treball núm. 03/2  José Ángel Zúñiga Vicente, José David Vicente Lorente Coordinador / Coordinator Documents de treball: Esteve van Hemmen http://selene.uab.es/dep-economia-empresa/codi/documents.html e-mail: stefan.vanhemm[email protected] Telèfon / Phone: +34 93 5812257 Fax: +34 93 5812555 Edita / Publisher: Departament d'economia de l'empresa http://selene.uab.es/dep-economia-empresa/ Universitat Autònoma de Barcelona Facultat de Ciències Econòmiques i Empresarials Edifici B 08193 Bellaterra (Cerdanyola del Vallès), Spain Tel. 93 5811209 Fax 93 5812555 Febrer / February , 2003 La sèrie Documents de treball d'economia de l'empresa presenta els avanços i resultats d'investigacions en curs que han estat presentades i discutides en aquest departament; això no obstant, les opinions són responsabilitat dels autors. 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ASSESSING THE STRUCTURAL CHANGE OF STRATEGIC MOBILITY DETERMINANTS UNDER HYPERCOMPETITIVE ENVIRONMENTS José Ángel Zúñiga Vicente José David Vicente Lorente Document de treball núm. 03/2 1 ASSESSING THE STRUCTURAL CHANGE OF STRATEGIC MOBILITY DETERMINANTS UNDER HYPERCOMPETITIVE ENVIRONMENTS José Ángel ZÚÑIGA VICENTE (*) Universidad de Salamanca Facultad de Economía y Empresa Departamento de Administración y Economía de la Empresa Campus «Miguel de Unamuno» 37007 Salamanca (SPAIN) Tel: (0034) 923 294400-Ext. 3502 Fax: (0034) 923 294715 E-mail: [email protected] José David VICENTE LORENTE Universidad de Salamanca Facultad de Economía y Empresa Departamento de Administración y Economía de la Empresa Campus «Miguel de Unamuno» 37007 Salamanca (SPAIN) Tel: (0034) 923 294400-Ext. 3502 Fax: (0034) 923 294715 E-mail: [email protected] (*) Address for correspondence. 2 ASSESSING THE STRUCTURAL CHANGE OF STRATEGIC MOBILITY DETERMINANTS UNDER HYPERCOMPETITIVE ENVIRONMENTS ABSTRACT The primary purpose of this exploratory empirical study is to examine the structural stability of a limited number of alternative explanatory factors of strategic change. On the basis of theoretical arguments and prior empirical evidence from two traditional perspectives, we propose an original empirical framework to analyse whether these potential explanatory factors have remained stable over time in a highly turbulent environment. This original question is explored in a particular setting: the population of Spanish private banks. The firms of this industry have experienced a high level of strategic mobility as a consequence of fundamental changes undergone in their environmental conditions over the last two decades (mainly changes related to the new banking and financial regulation process). Our results consistently support that the effect of most explanatory factors of strategic mobility considered did not remain stable over the whole period of analysis. From this point of view, the study sheds new light on major debates and dilemmas in the field of strategy regarding why firms change their competitive patterns over time and, hence, to what extent the ‘contextdependency’ of alternative views of strategic change as their relative validation can vary over time for a given population. Methodologically, this research makes two major contributions to the study of potential determinants of strategic change. First, the definition and measurement of strategic change employing a new grouping method, the Model-based Cluster Method or MCLUST. Second, in order to asses the possible effect of determinants of strategic mobility we have controlled the non-observable heterogeneity using logistic regression models for panel data. Key words: strategic mobility, dynamic analysis, structural change, hypercompetitive environments, MCLUST, ‘logit’ model with panel data. 3 1. INTRODUCTION During the last two or three decades companies in almost every sector of economy are coping as best as they can to the continuous and dramatic environmental changes. As a direct result of these environmental revolutions a large number of organizations have disappeared, while other companies have emerged and many others are continuously engaged in massive strategic reorientations. In fact, strategic change is frequently considered both by managers and scholars as one of the primary forces which can help organizations to continue surviving in these new types of environments, recently described by some researchers as ‘hypercompetitive environments’ (Ilinitch et al., 1996). Therefore, in this context the knowledge of potential determinants of strategic change in organizations and their implications seem today more needed than ever. Since late 1970s a large body of theoretical and empirical literature has been devoted to understand the different factors motivating and constraining strategic change in organizations (Kelly & Amburgey, 1991). However, after more than two decades of research on this issue a set of contradictory findings continue emerging. Several theoretical and methodological differences are usually used to explain these ambiguous results (Ginsberg, 1989; Lewin & Volverda, 1999; Rajagopalan & Spreitzer, 1996). From a strictly theoretical point of view, a primary difference is based on “the extent to which researchers adopt an adaptive or inertial view of strategic change” (Boeker, 1997: 152). Theorists who argue for the predominance of strategic adaptation frequently emphasize the important role that managers play in monitoring environmental shifts and modifying organizational strategy to better match new environmental conditions (Andrews, 1971; Ansoff, 1965; Chandler, 1962; Child, 1972; Miles & Snow, 1978; Thompson, 1967). More recently, from this research stream it is also explicitly recognised the role that certain internal factors –resources– can play in the strategic adaptation process (Barney, 1991; Teece et al., 1990, 1997; Wernerfelt, 1984, 1989). Proponents of inertial perspective of strategy, in contrast, usually argue that firms can be constrained in their ability to adapt to new environmental conditions, that the managers’ role in the adaptation process is not relevant, and that it is the general tendency for strategy to be preserved rather 4 than radically changed depending on certain organizational factors such as firm size, age and structural complexity (Hannan & Freeman, 1984, 1989). From a methodological view, researchers have used different methods –in terms of samples of firms, time periods of study, variables and measures of variables, cross-sectional or longitudinal and dynamic character of study, statistic analysis applied, etc– in order to test what are the main factors for and against strategic change. This also explains why results across different studies are contradictory. Notwithstanding theoretical and methodological differences, most of empirical research carried out until now has been conducted under the explicit or tacit assumption that empirical models are stable over the period analysed. We test this particular premise by assessing the structural stability of a number of models in a scenario characterised by very relevant environmental transformations. On the other hand, empirical insights on outcomes of strategic change in highly volatile environments are commonly claimed to be a favoured scenario because of the common belief that strategic change among relevant actors increase. However, as noted above the great majority of conventional longitudinal models used to find empirical regularities heavily rely on the assumption that statistical and distributional properties of each model are stable over time. Under our view this can reflect a clear methodological inconsistency. Since profound changes in environmental conditions can significantly alter market boundaries, technological choices, organizational structures and competitive strategies, we think that after more than three decades of contradictory findings this is the moment to formulate the following question: Does it make sense to assume that the effect of potential factors for and against strategic change remains stable over time under rapidly changing environmental conditions? Although, in some way, there are several case histories which have offered descriptive support about the instability of determinants of strategic change, to date, no quantitative empirical research has explicitly tested this important issue yet. Therefore, this exploratory study offers one of the first empirical attempts to fill this important gap in the literature on strategic change. After revising the premises of some of the most popular theories on strategic change, we develop a dynamic and additive 5 framework that may represent a promising beginning toward building a new theory of strategic change, where time dimension or ‘context’ should play a crucial role. Indeed, this framework can help researchers to assess the temporary validity of each theory and provide an alternative way to see to what extent both adaptation and inertia must be really considered as complementary or ‘contextdependent’ views to analyse strategic change. We looked for an answer to the above question in a setting characterized by very relevant changes in their environmental conditions for a long time period as a direct consequence of the deregulation process undergone: the Spanish private banks during the years 1983-1997. The paper has four main sections. In a second section, the theoretical and empirical literature on the antecedents of strategic change is briefly reviewed. The research methodology is developed in the third section to answer the question above. The results are presented in the fourth section. In the fifth and final section, we discuss the most important conclusions and implications of our study and consider possible avenues for future research in this field. 2. THEORY AND EMPIRICAL EVIDENCE OF STRATEGIC CHANGE DETERMINANTS 2.1. The adaptation perspective Historically, the dominant view in the study of strategic change has been an adaptation perspective (Singh et al., 1986: 587). Although the contingency approach (Lawrence & Lorsch, 1967; Thompson, 1967), the institutional theory (DiMaggio & Powell, 1983; Meyer & Rowan, 1977; Zucker, 1989) and the resource dependence theory (Aldrich & Pfeffer, 1976; Pfeffer & Salancik, 1978) have also been interested in examining the main facilitating and inhibiting factors of strategic change, it is an essential subject in the classic strategic management approach (Andrews, 1971; Ansoff, 1965; Chandler, 1962; Porter, 1980), the strategic choice theory (Bourgeois, 1984; Child, 1972, 1997; Miles & Snow, 1978, 1994) and, more recently, in the resource-based view (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984) and the dynamic capabilities approach (Hamel & Prahalad, 1994; Teece & Pisano, 1994; Teece et al., 1990, 1997). 12 Although liberalization process in Spanish banking advanced significantly in the 1970s it accelerated since the 1980s, mainly after joining the European Community (EC) in 1986. From this date many legal restrictions were progressively suppressed, especially those conditions associated with the European banks. Before this date they were subjected to three restrictions. First, they could not obtain financing (through deposits, for example) in Spain for more than 40 per cent of the credits given to Spanish residents (the inter-bank market was excluded from this restriction). Second, they could not open more than three branch offices, including the main office. Third, their portfolio of securities had to be of government issues. In 1987 all interest rates and service charges were liberalized. Since 1988 geographical limits imposed on savings banks to expand theirs number of branch offices were totally removed. This meant a major competition between this type of entities and private banks. From this date, the Spanish government introduced legal changes that allowed banks to drastically lower their reserve coefficient, because maintaining high percentages would jeopardize the competitive position of Spanish banks in relation to the other European countries. In 1989 a remarkable change in the policy environment took place: the complete liberalization of capital flows, in the context of the entry (June, 1989) of the Spanish currency (peseta) into the exchange mechanism of the European Monetary System (EMS). In addition, an open price war broke out between the major Spanish banks. The period 1989-1992 witnessed several important mergers among the major Spanish banks, as well as some minor operations involving a large number of small savings banks. Important changes in behaviour of the clientele also started to occur. Another very important institutional change in the evolution of the European Union countries’ banking system was connected to the establishment of the Single European Market, in 1993. Since 1st January 1993 Spanish authorities had to authorize any bank, Spanish or EU, as long as the candidate satisfied the given legal conditions, and their discretionary power was abolished. The period 19921995 witnessed the last important economic crisis in Spain and other industrialised countries. As 13 previous economic crisis, it seriously affected banking institutions owing to the restrictive monetary policy carried out by the Spanish government and especially by the European monetary authorities. Although over the 1970s and 1980s technological innovation was very significant it accelerated rapidly from the mid 1990s as a direct result of the introduction of the Internet in the banking business. Lastly, this important technological revolution has significantly increased competition by lowering entry barriers among different industries in the financial services sector. It has increased the speed of information processing, the complexity and the launching of new financial products and services. Consequently, under these relevant regulatory, socio-economic and technological changes, it seems reasonable to infer a high strategic mobility in Spanish private banks since all of them have increased the level of competitive rivalry. The final purpose of this strategic reorientation process is to achieve a suitable fit with this new type of hypercompetitive environment in order to continue surviving. Thus, the population of Spanish private banks is an excellent research arena for studying whether the potential promoting and inhibiting factors of strategic change have remained stable over time under highly turbulent environments. 3.2. Data collection Data used in our empirical study has been mostly taken from the yearly reports published by the Higher Council of Banking and the Spanish Banking Association. These reports provided detailed balance data and other supplementary information from 1983 to 1997. All data referred to the end of the corresponding year. Our resultant data set contained 1264 observations of 134 different banks over the 15-year period considered1. 3.3. Variable Measures Dependent Variable: The dependent variable of interest in this research signalled the event of a strategic change or strategic move. We followed a three-stage procedure for operationally defining this variable. First, we chose the variables which best captured the competitive banking strategy. Next, we 14 identified strategic groups for defining, in the last stage, the representative variable of a strategic move (see Figure 1). Insert Figure 1 about here In a first stage, we identified the dimensions on which banking strategy is defined. We believe, as do Cool and Schendel (1987: 1109), Fiegenbaum et al., (1990: 136) and Fiegenbaum and Thomas (1990: 198), that the specification of strategy variables normally depends on the industry under investigation and it requires a clear and thorough understanding of industry economics and the range of competitive strategies adopted by competing firms. For the population selected in this study, the competitive strategy is usually associated with three different kinds of decisions: (a) the financial products and services offered by these banking firms; (b) the customers served; and (c) the scope commitments. Finally, according to Caminal et al., (1993) these decisions make up the competitive strategy of these financial entities in relation to their market segments. In view of the fact that in this work we only include Spanish private banks, the basic determinants of their competitive strategies will be decisions linked to the financial products and services offered and the main market segments served. The final selection of competitive strategy dimensions was carefully identified in two phases (see Figure 1). In a first phase, we carried out a detailed revision of the literature existing in Spain and other countries on the banking industry. In a second phase, after discussion with several industry executives and experts we chose the specific strategic variables which might best represent the strategic behaviour followed by Spanish financial institutions. These dimensions –which were summarized in seven key variables at a business level–, were collected from the balance of these financial organizations. Here, our main assumption, in the line of prior research on the banking industry (e.g., Amel & Rhoades, 1988; Caminal et al., 1993; Más, 1999; Mehra, 1996; Passmore, 1986) is that the balance composition of each bank can be an accurate representation of the different 1 We employed the following criteria for selecting these yearly observations: number of employees, staff expenditures and 15 financial products and services offered in its market segments. This has been a traditional way of defining and measuring the competitive strategy in the banking sector. In Table 1 we show the strategic variables chosen and their relationship to the major market segment and banking strategy performed. Insert Table 1 about here By observing the three former strategic variables (V1, V2 and V3) we can distinguish among three different types of Spanish financial institutions according to their capacity to provide financial funds. The first ones have a clear commercial inclination and their target segment is usually made up of households and different types of firms (Commercial Banking, which is associated with a large percentage of commercial loans: V1). The second ones appear to be industry-oriented since they have significant investment in securities and their main target segment may be characterized by transactions in the stock markets (Investment Banking, which is related to a high proportion of securities portfolios: V2). The last ones have an institutional calling and their main target segments are the financial markets in general (Institutional Banking, which is associated with a high percentage in treasury: V3). On the other hand, the four remaining strategic variables (V4, V5, V6 and V7) allow us to distinguish among three different types of banking activities according to the means selected for obtaining their financial resources. In this sense, there are banks that have firms and households as primary market targets for accessing funds. Their basic services consist of issuing low-yield and stable liabilities which do not require intensive commercial or design efforts (Traditional Banking, which is associated with a great percentage of savings and deposits accounts: V4), or issuing liabilities which require larger resource investment in their sale and design (Innovative Banking, which is related to a high proportion of current accounts, but especially to other accounts: V5 and V6). Conversely, other Spanish private banks heavily rely on the inter-bank market for obtaining funds (Credit-Debt Position in the financial system: V7). This variable can take both positive and negatives values. If it takes positive values it number of branch offices in each bank had to be greater than zero for each year under study. 16 means that a bank has a credit position, while if it takes negative values it means that a bank has a debt position in the financial market. In a second stage, we identified strategic groups in the population of Spanish private banks. For this, we used a procedure similar to that of Cool and Schendel (1987), Fiegenbaum et al., (1990), Fiegenbaum and Thomas (1990, 1993), Más (1999) (see Figure 1). Firstly, it was necessary to identify the periods of homogeneity (Stable Strategic Time Periods, SSTPs hereafter). Next, we clustered banks into strategic groups for each SSTP. The procedure to identify strategic groups is based on the proposition that banks having a similar strategic positioning (in terms of the seven key strategic variables), will be clustered in the same strategic group. In this case, we employed a new grouping method –the Model-based Cluster Method or MCLUST (Banfield and Raftery, 1993; Fraley and Raftery, 1998a). In a third step, we interpreted and characterized the strategic groups over time to try to discover the specific competitive strategy followed by each banking entity in every SSTP, between 1983 and 1997. In the last stage, we assessed whether a bank had changed it competitive strategy over the period of study (see Figure 1). We assumed that a bank experienced a strategic move if it changed its competitive position from one strategic group to another between two successive SSTPs. This followup of each bank over the period of study was used to calculate the dependent variable of interest in our study, strategic move or strategic change. It was coded as a categorical variable that took a value of 1 in case of strategic move across strategic groups, and 0 otherwise. In other words, a bank with a value of 0 for a given year has undergone no moves across strategic groups regarding the preceding SSTP. Independent Variables Given the exploratory purpose of this research we do not make ‘a priori’ support for the adaptation or inertial perspectives. Conversely, we formulate an additive framework. Mainly, our empirical models include the following four groups of factors for assessing their potential effect on the Spanish private banks’ strategic mobility: environmental characteristics, organizational and firmspecific factors, and finally some managerial characteristics such as the CEO succession event. 17 Environmental Characteristics: In accordance with previous empirical studies (e.g., Baum & Korn, 1996; Delacroix & Swaminathan, 1991; Ruef, 1997; Tucker et al., 1990; Wholey & Burns, 1993) we introduced two specific environmental factors to capture the competitive rivalry existing in the Spanish private banks. These factors were related to the industrial environment of this industry: concentration and density. Herfindahl concentration index, i.e. the sum of squares of market shares of banks, in terms of its credits, was used as a measure of concentration. Density was measured as the count of Spanish private banks, foreign banks and savings banks. We considered the linear and quadratic term of density for exploring the form of relationship between this variable and strategic change. All these variables were calculated in the previous year to a bank’s strategic move. Organizational Variables: Three organizational variables were included as possible promoting and inhibiting factors of Spanish private banks’ strategic change: bank age, size and structural complexity. Following prior empirical works (e.g., Baum & Korn, 1996; Kelly & Amburgey, 1991; Stoeberl et al., 1998; Zajac & Kraatz, 1993), we measured bank age as the number of years since founding. As in other research (e.g., Haveman, 1993; Kelly & Amburgey, 1991) we used the natural logarithm of banking assets as an indicator of the bank size. Finally, we measured structural complexity in terms of number of branch offices, such as Gresov et al., (1993: 197) posit. All these variables were measured for the previous year to a bank’s strategic move. Firm-Specific Variables: Among this type of variables we distinguish between variables associated with economic performance and management skills, and variables linked to other internal factors. Within the former we included four indicators which have been usually used within the banking sector: leverage, return on assets, labour costs per employee, degree of employee qualification and number of liability accounts managed per employee. Leverage was defined as the bank debt to its capital equity. We measured return on assets as the ratio of net income to total assets. Labour cost per employee and the number of liability accounts managed per employee were usual measures for banking efficiency and productivity (e.g., Berger & Mester, 1997; DeYoung & Hasan, 1998; GrifellTatjé & Knox, 1996). We employed the ratio of number of executives and graduates to the total 18 number of employees as an indicator of degree of employee qualification. Finally, within other internal and idiosyncratic factors we introduced the ratio of fixed assets to the number of branch offices as an indicator of a bank’s productive capacity, and other two variables to capture the importance of several resources related to a bank’s reputation: customer loyalty and brand loyalty. We defined the first variable as the ratio of time deposits to the total deposits. The second one was defined as the number of years that the bank has maintained its trademark divided by its age. These eight firmspecific variables were measured for the previous year to a bank’s strategic move. Chief Executive Succession: It was coded as a dummy variable which took value of 1 when a succession event occurred the previous year to a bank’s strategic move and 0 otherwise. 3.4. Identifying the strategic positioning of Spanish private banks over time As noted above for clustering banks into similar strategic groups in each of the potential SSTPs, we employed the MCLUST or Model-based Clustering, which has the following advantages over other clustering procedures. First, this technique has a statistical basis, which allows for inference. It is, for example, possible to derive uncertainty estimates for individual classifications as well as for the clustering as a whole. Second, several criteria can be used to assess the optimal number of clusters, a direct consequence of the statistical model used to describe the data. This is a large advantage compared to hierarchical clustering methods, for example, where a cut-off value must be chosen by the researcher. In most cases, no clear criteria exist for such a choice. Third, the clustering method can be selected according to the same criteria used for the choice of the number of clusters. As in the case of hierarchical clustering, several closely related clustering methods exist, and the one that fits the data best can be distinguished in an objective way. These properties of MCLUST are particularly useful for our research purpose. Traditional hierarchical clustering methods depend critically upon quite discretional parameters (e.g. the number of clusters and the type of distance between objects) which lack an ‘a posteriori’ statistical validation. In our case, we must compute a total of 15 ‘clusterings’ (one per year) and, therefore, the number of discretionary choices is large enough to induce arbitrary results in the number and composition of clusters. Alternatively, the MCLUST algorithm is free from 19 purely discretionary choices since a statistical criterion can consistently be applied for detecting the number of clusters as well as the distributional properties of objects in the sample. The model underlying MCLUST assumes that the analyst faces a heterogeneous number of objects that can be accurately represented by a mixture of normal (Gaussian) multivariate distributions. In our case, the value of the multidimensional vector of the proposed clustering variables (V1 to V7) for a given bank in period “t” is assumed to be the realization of a multivariate normal distribution N(µk,Σk), where µk and Σk are the mean vector and the covariance matrix respectively of cluster k. In the case of G clusters the likelihood function is given by: ),|()|,,( 1 1 kkik G k k n i xxL Σ=Σ ∑ ∏= = µφπµπ , where πk is the fraction of objects belonging to cluster k. The problem is that the classes of the objects xi are unknown. The Expectation-Maximization (EM) algorithm by Dempster et al., (1977) iteratively solves this problem. The first step in the EM algorithm for mixture models (McLachlan & Krishnan, 1997) is the calculation of pik, the conditional probability that object i belongs to class k, given an initial guess for parameters π, µ, Σ: ),,|( ),,|( 1 ∑ = Σ Σ =k j ij ik ik x x p µπφ µ π φ This is done for all objects and for all classes. This is the expectation step; the second step is the maximization step, in which the parameters π, µ, and Σ for the mixture model are estimated. The pik are used in this estimation, and therefore these parameters may differ from the initial estimates. The Eand M-steps alternate until convergence. Eventually, objects can be classified into the cluster with pik the highest value for that object: maxk (pik). Usually, one does not start the EM algorithm with initial values for π, µ, and Σ, but with an initial partitioning and the M-step. Since the choice for an initial partitioning can influence the eventual classification significantly (McLachlan & Krishnan, 1997), most applications start from a number of different starting points and use the one leading to the best 20 clustering, or use another clustering method to obtain the initial classification. In this case, fast hierarchical methods are used for initialization. Because the data are described by a statistical model rather than a heuristic procedure, it is possible to choose the optimal number of clusters and the “best” clustering model. The likelihood of the classification is a first indicator, but it fails to incorporate the complexity of the model; more complex models will find it easier to fit the data well. Several measures that correct for this are available, of which Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are the most well known. The AIC (Akaike, 1974) is given by AIC=-2logL+2np, where L is the likelihood and np the number of parameters in the model (here π, µ and Σ); the model that minimizes the AIC value is picked. The AIC tends to overestimate the number of clusters, but it is still often used in practice. Alternatively, the BIC (Schwarz, 1978) is given by BIC=2logLnp log n, where n is the number of objects. Compared to AIC, BIC will select models with fewer parameters, hence more parsimonious models (at least for cases where n>8). In the MCLUST software used in this study the BIC value permitted us to choose the optimal model and number of clusters. It should be noted that the “optimal model” and “optimal number of clusters” are used here in the sense of “best describing the data”; whether this is also optimal in terms of interpretation of the clustering should be assessed afterwards. Calculations in this research were performed by the MCLUST package for model-based clustering by Fraley and Raftery (1998b)2. 3.5. Defining and measuring the effect of independent variables on strategic change The empirical tool for testing the effects of different independent variables depends on the definition of the dependent variable. In this study, the frequency or probability of strategic change was assessed by logistic regression using panel data techniques. Conversely to the OLS regression, panel data estimators overcome inconsistency problems when there are observation-specific effects (i.e. bankspecific) on the dependent variable. This fact is likely to occur in our research object (strategic change) since some idiosyncratic factors are relatively stable through the period analysed and they 2 The original program (http://www.stat.washington.edu/fraley/mclust/soft.html) is an add-on package for S-plus (http://www.insightful.com). A more complete account of currently available software for mixture modelling is given in McLachlan and Peel (2000). 21 may substantially affect the strategic change chances of the bank such as its property and governance structure. The control of these unknown and specific effects in logistic models is even more important than in linear models, as in the latter consistent estimates can be obtained if the unobservable effects and random perturbations are uncorrelated, while such a condition does not preserve consistency for non-linear specifications, e.g., the ‘logit’ model (Greene, 1997: 888). Similarly to the linear case, logistic regression for panel data may lead to different specifications depending on the nature, random or deterministic, which is assumed for the firm-specific effects. A general specification of logistic regression for panel data can be expressed in the following way: Yit* = Vi + β’Xit + Uit , where Yit = 1 if Yit* > 0 and Yit = 0 otherwise, where Yit is observable but not Yit*. The idiosyncratic effect is denoted Vi and β represents the coefficients to be estimated. The random perturbation, Uit, follows a logistic distribution and the assumptions about Vi specify the model completely: the fixed-effects model considers Vi as a constant that differs for every individual. Alternatively, random-effects estimates are derived after assuming that Vi = εit* + εi, where εit* and εi are uncorrelated random variables. From a strictly theoretical perspective, the choice of one of the above specifications over the other (Fixed vs. Random) depends on the likelihood of the assumptions for each particular case3. Obviously, this question is very important when the estimates differ widely between the two models. Nevertheless, there are some methodological peculiarities of logistic panel data models that favour random effects estimation due to sample characteristics. Maximum likelihood estimation of a fixed effects model is unaffected by observations in the sample with ‘time-invariant’ response. In other words, the estimation procedure of a fixed-effects model excludes all observations with ‘ones’ or ‘zeros’ for every year. This would lead, in our case, to an important reduction in the sample size and, thus, comparative analysis among estimates can lead to misinterpretation. Then, after evaluating this 28 strategic change process when the environmental conditions are experiencing dramatic transformations. In others terms, the findings of this exploratory study suggest that the “contextdependency’ of alternative or competitive explanatory theories of strategy change as their relative validation can significantly vary over time for a given population of firms. From a strictly methodological point of view, this work makes two major contributions to the study of determinants of strategic change. Firstly, we have benefited from the great potential of the MCLUST grouping algorithm used to determine the strategic groups as a previous step for defining the dependent variable of interest: strategic move or strategic change of firms. The basics of this grouping method allow researchers to determine critical outcomes from cluster analysis in a more objective mode, i.e. the number of the resulting groups. Secondly, regarding the regression analysis, we also propose the use of panel data formulations in order to control effectively unobservable firmspecific effects likely to distort estimates when they are not controlled as occurs when employing traditional cross-sectional models of logistic regression. Three primary extensions for future research on the stability of determinants of strategic change can be exposed. Firstly, our collection of potential determinants of strategic change is far from being detailed and additional factors should be explored. In this sense, we are aware that our review may be comprised by a limited identification of the possible determinants of strategic change. Hopefully, it would be very interesting to investigate in future research the influence of other potential determinants not included in this study. Secondly, additional empirical evidence in different industries or environmental conditions would help to assess the robustness or generalization of our findings. Finally, we recognize that, perhaps, the most important contribution of this study is empirical since it has been designed as an exploratory research. 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The change of any coefficient of the model between periods can be tested by performing the following test in the above model: H0: φj = 0 for all j (time-invariant coefficients). H1: φj ≠ 0 for any j (period-dependent coefficients). The change of a single coefficient of the model between periods can be tested by performing the following test in the above model: H0: φj = 0 for all j (time-invariant coefficient of variable j). H1: φj ≠ 0 for all j (period-dependent coefficient of variable j). 35 TABLE 1 STRATEGIC VARIABLES: DEFINITION, MARKET SEGMENT AND BUSINESS BANKING STRATEGY Strategic variables Definition Market Segment (Business Banking Strategy) V1Commercial Loans / Financial Investments Lending Market (Commercial Banking) V2Portfolio of Securities / Financial Investments Lending Market (Investment Banking) V3Treasury / Financial Investments Lending Market (Institutional Banking) V4Savings and Deposits Accounts / Borrowed Capital Instrument Saving Market (Traditional Banking) V5Current Accounts / Borrowed Capital Instrument Saving Market (Innovative Banking) V6Other Accounts / Borrowed Capital Instrument Saving Market (Innovative Banking) V7Net Position in Financial Markets / Total Liabilities Inter-bank Market (Creditor-Debtor Position) 36 TABLE 2 STRATEGIC GROUPS (SGs) OVER TIME 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Number of SGs 5 6 7 5 5 8 8 11 710 910 9 8 6 Number of new SGs 0 2 5 0 0 3 2 3 1 3 1 1 0 0 0 Number of SGs disappeared 0 1 4 2 0 0 2 0 5 0 2 0 1 1 2 Net change in number of SGs 0 1 1 -2 0303-4 3-1 1-1 -1 -2 Number of SGs that persist 0 4 2 5 5 5 6 8 6 7 8 9 9 8 6 Percentage of firms undergoing strategic change 00.41 0.90 0.62 0.31 0.44 0.55 0.51 0.77 0.55 0.45 0.41 0.32 0.4 0.29 37 TABLE 3 DESCRIPTIVE STATISTICS Variables Number of observations Mean Standard deviation Minimun Maximun Strategic change 1264 0.50237 0.50019 0 1 Concentration 1264 0.07468 0.00989 0.05928 0.09013 Density 1264 210.1297 4.08100 203 219 Density2 1264 44171.15 1719.651 41209 47961 Bank age 1264 59.27136 49.44264 0279 Bank size 1264 11.46048 1.65515 7.23562 16.32363 Number of branch offices 1264 173.2342 417.2486 13493 Productive capacity 1264 92.7263 237.7792 03303.75 Leverage 1264 3.72569 26.16719 0501.25 Return on assets 1264 0.00881 0.03079 -0.31421 0.49358 Labour costs per employee 1264 4.81197 2.68429 0.97 48.33333 Degree of employee qualification 1264 0.48004 0.13869 0.11111 1 Liability accounts per employee 1264 156.8808 135.6348 02027.074 Customer loyalty 1264 0.36562 0.24510 00.98433 Brand loyalty 1264 0.54087 0.40964 0 1 CEO succession 1264 0.17326 0.37862 0 1 02/9 Governance Mechanisms in Spanish Financial Intermediaries Rafel Crespi, Miguel A. 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