Relationship between management information systems and corporate performance
The literature review on the success of management information systems (IS) provides empirical evidence that mere investment in IS and New Management Tools (NMTs) does not guarantee better business results. Aiming to contribute to the knowledge of the factors explaining the success of IS implementation, this paper classiﬁes them through cluster analysis, with a sample of Spanish companies according to the valuation given by their ﬁnance directors (CFOs) to the quality of such systems and their use for strategic purposes. This classiﬁcation helps to answer three questions: do companies that better rate their IS improve their performance? How do IS quality and strategy affect results? Is there a positive relationship between the use of NMTs and improvement in performance?
Through the non-parametric Kruskal–Wallis test and a partial least squares (PLS) model results are yielded that support the ﬁrst question and show the positive effect of the IS quality and strategy on improving corporate proﬁtability. Logistic regression showed an interaction between the use of NMTs and the IS strategic approach with positive effects on improving proﬁtability. The results of this study have signiﬁcant implications for companies, suggesting that investment in new IS and NMTs must be coupled with a clear sense of strategy.
Nowadays, the environment is extremely competitive and globalized, and technologies are evolving constantly. Firms need more effective and sophisticated management accounting systems to successfully face the new conditions and improve their ﬁnancial performance (Al-Omiri & Drury, 2007; Gupta & Gunasekaran, 2005; Libby & Waterhouse, 1996; Mia & Clarke, 1999).
In recent years, increasing global competition has intensiﬁed the challenges faced by managers, and many experts warn that management accounting needs to adapt to meet managers’ changing needs if it is to maintain its relevance (Chenhall & Langﬁeld-Smith, 1998a). Many innovations in management accounting have been introduced in response, in an attempt to improve its utility.
Traditional techniques in management accounting, such as sections costs, budgets, standard costs, and direct costs have been combined with more recent techniques over the last three decades. There is no universal consensus on which techniques constitute New Management Tools (NMTs) (Cadez & Guilding, 2008). Nevertheless, most authors consider as NMTs or non-traditional techniques: activity-based costing (ABC), activity-based management (ABM), balanced scorecard (BS), just in time (JIT), total quality management (TQM), target costing (TC), strategic management accounting (SMA), lifecycle costing (LCC), benchmarking and theory of constraints (TOC). The prevalence of these techniques indicates that ﬁrms need increasingly accurate and sophisticated management information systems (IS) that adapt to managers’ changing needs.
Researchers assume that managers, as rational agents, are unlikely to adopt a management IS that does not help them improve their ﬁrm’s ﬁnancial performance (Chenhall, 2003). Thus, management information will conceivably help improve decision-making and, as a consequence, ﬁnancial performance. Likewise, ﬁrms that rate their management IS highly will conceivably adopt NMTs to a greater extent, with the ultimate objective of maintaining and/or improving their ﬁnancial performance. The current piece of work follows the approach of the abovementioned contributions to the accounting literature and considers that a management IS is successful if it enables the ﬁrm to take better decisions and improve its ﬁnancial performance.
Internal accounting IS differ between companies, for example, in terms of quality, level of use and strategic relevance. Studies in the accounting literature tend to focus on the impact of speciﬁc management techniques on ﬁnancial performance, while few look at the evaluation ﬁrms make of their own IS and the relation of these to ﬁnancial performance. Empirical evidence shows that investment in NMTs does not guarantee better results. The mechanisms through which IS affect a ﬁrm’s performance are therefore under- examined. This study aims to contribute to this line of research by analyzing to what extent quality and the strategic approach of IS improve ﬁrm’s performance, evaluating the effect that the use of NMTs has on performance.
This study evaluates the management IS of a sample of Spanish ﬁrms on the basis of the scores that their ﬁnancial directors (CFOs) give in two areas: quality of IS (IS quality) and strategic use of the IS (IS strategy), which are identiﬁed in a principal components analysis. We use these elements to accomplish a cluster analysis, which identiﬁes three different types of ﬁrms depending on their management IS. This typology of ﬁrms is then used to answer the following questions:
- Do ﬁrms whose management IS scores highly improve their performance?
- How do IS strategy and IS quality affect ﬁrms’ performance?
- Does a positive relationship exist between the use of NMTs and increased proﬁtability?
The following section analyses the success of IS, their relation- ship with economic results and the effect of new tools or techniques (henceforth “techniques”, NMTs). Then, the research hypotheses and the methodology followed are described, including the sample and the variables used. The ﬁfth section presents the results of the empirical study, while the ﬁnal section offers the most important conclusions of the research and its limitations.
This article deals manly with three basic concepts: the success of IS, ﬁnancial performance, and the relation between NMTs and performance. First, we will analyze the literature dealing with success in IS, focusing on its effect on corporate results. NMTs are also taken into account.
Success of information systems
This work aims to evaluate the success of management IS and of NMTs; hence, the ﬁrst step is to deﬁne what is meant by success in this context.
The evaluation of IS is a difﬁcult task for researchers (Limayem, Banerjee, & Ma, 2006; Serafeimidis & Smithson, 2000). Similarly, deciding whether an IS or management technique is successful is by no means simple either. According to Petter, DeLone, and McLean (2008), measurement of IS success is both complex and illusive. Thus, for example, it is extremely difﬁcult to deﬁne what success is in the case of ABC (Shields, 1995), and some apparent failures of a particular technique may in fact be a consequence of a limited appreciation of the uses for which it was put into practice (Malmi, 1997).
DeLone and McLean (1992) examine the literature on the success of IS and conclude that researchers do not use a single measure of success, but various. These authors established a success evaluation method from 6 different and interrelated dimensions. Later, DeLone and McLean (2003) updated and improved the previous model with 7 variables or dimensions to measure IS success: information quality, service quality, system quality, intention to use, use, user satisfaction and net beneﬁts. These models have been widely used by IS researchers for understanding and measuring the success of IS.
User satisfaction is one of the most important measures of IS success (Urbach & Müller, 2012); it remains, however, an uncertain concept (Livari, 2005). IS users expect the system to be of high quality, to have quality information and to provide substantial beneﬁts (Wu & Wang, 2006). The main determinants of user satisfaction with IS are relevance, content, accuracy, and timeliness (Seddon & Yip, 1992). These elements were all gathered in the IS survey con- ducted for this study. It is therefore understood that a high score of these factors is related to high IS user satisfaction (in this case, CFOs).
One possible way of evaluating the success of an IS is to deter- mine if its objectives have been met. In other words, if the ﬁrm has achieved the beneﬁts that theory suggests it would achieve. This is difﬁcult to decide because such systems often lack clearly deﬁned speciﬁc objectives. The objectives are usually generic, such as to improve the process of decision-making, which is extremely difﬁcult to test a posteriori.
Accountancy literature has not reached a consensus about the objectives of IS. In a global context, most objectives can be considered intermediate. That is, they are not the ﬁnal goals but rather stepping-stones on the road to the ﬁrm’s ultimate objective. This is generally assumed to be to ultimately obtain the greatest possible proﬁt, or more speciﬁcally to achieve sustainable improvements in proﬁtability (Chenhall, 1997). This amounts to saying that no ﬁrm would want to implement a new management IS if it did not expect the system to ultimately generate an improvement in its ﬁnancial performance, even if the ﬁrm adopts the system with some speciﬁc objectives of management improvement. When a ﬁrm commits to implementing, using, and supporting an IS, the ﬁrm often does so because some type of positive organisational impact is desired, such as improved proﬁtability or productivity (Petter, DeLone, & McLean, 2013).
Information systems and performance
As has been suggested previously, the success of the IS can be obtained by measuring its effect on results. Various authors agree with this idea, and afﬁrm directly that the aim of a management IS should be to achieve an improvement in the ﬁrm’s ﬁnancial performance. For instance, authors say that ABC should help ﬁrms take better decisions or improve their ﬁnancial performance (Dopuch, 1993); the objective of ABC is to improve ﬁnancial performance, not to obtain more exact costs (Cooper & Kaplan, 1992); ﬁrms adopt an innovation to achieve beneﬁts that directly or indirectly affect ﬁnancial performance indicators (Cagwin & Bouwman, 2002); or the main objective of an IS is to improve and enhance the potential role of the system in improving the ﬁrm’s overall ﬁnancial performance (Ranganathan & Kannabiran, 2004). Using ﬁnancial performance as an indicator of the success or failure of IS has various advantages. On the one hand, performance measurement is critical to the success of the ﬁrm because it creates understanding, shapes behaviour, and improves competitiveness (Gunasegaran, Williams, & McGaughey, 2005). On the other hand, ﬁnancial performance represents a common objective of all the ﬁrm’s IS and/or management techniques, which makes it easier to evaluate their utility. Finally, despite their limitations, ﬁnancial data have the advantage of being precise and objective (Parker, 2000), while intermediate, non-ﬁnancial goals are often subjective, since they depend on personal opinions. Hence, the evaluation of non-ﬁnancial goals may depend on the job held by the respondent (Anderson & Young, 1999). Given the above advantages, the current study uses the ﬁrm’s ﬁnancial performance to measure the success of management IS and NMTs.
New management techniques and performance
Firms adopt NMTs with the purpose of improving the decision- making processes, their ﬂexibility and output costs, and, ultimately, to improve results (Henry & Mayle, 2003; Hatif AlMaryani & Sadik, 2012). Despite the limitations, a number of empirical studies attempt to relate ﬁnancial performance to management IS or NMTs. The majority of them analyse the individual effect of a particular management technique, albeit with a degree of divergence in results.
Some authors ﬁnd that a set of management techniques and management accounting practices improve ﬁnancial performance if ﬁrms follow certain strategic priorities (Chenhall and Langﬁeld- Smith, 1998b; Naranjo-Gil, 2004). In contrast, other empirical studies ﬁnd that ﬁrms that use traditional management accounting techniques are more proﬁtable than those that use NMTs (Chenhall & Langﬁeld-Smith, 1998a). Abernethy and Bouwens (2005), citing various studies, observe increasing evidence that innovation in management accounting does not improve either the decision-making or the ﬁrm’s ﬁnancial performance. Theodorou and Florou (2008) analyse the effect of a particular information technology (IT) on ﬁnancial performance considering ﬁve types of strategy, and in all the strategies they ﬁnd improvements in ﬁnancial performance when ﬁrms use advanced ITs. Therefore, there are difﬁculties providing evidence on a positive relationship between IT investments and ﬁrms’ ﬁnancial performance (Ismail, 2007; Mahmood & Mann, 1993).
The three NMTs that are most used by the sample ﬁrms are TQM, BS, and ABC. Various empirical studies have analysed the possible relationship between applying TQM and ﬁnancial performance; although some ﬁnd no relation between the two variables (Corredor & Goni, 2011; Ittner & Larcker, 1995), or only a partial relationship (Samson & Terziovski, 1999), the majority conclude that a positive relationship exists between the TQM technique and the ﬁrm’s ﬁnancial performance (Choi & Eboch, 1998; Easton & Jarrell, 1998; Lam, Lee, Ooi, & Lin, 2011; Sila, 2007), although some authors consider that such a relationship is negative (Wali & Boujelbene, 2011).
Few studies have investigated the possible relationship between the use of BS and ﬁnancial performance. This system has been shown to lead to superior ﬁnancial performance in comparison to traditional results measurement systems based only on ﬁnancial measures (Chi & Hung, 2011; Davis & Albright, 2004; De Geuser, Mooraj, & Oyon, 2009). Braam and Nijssen’s (2004) ﬁndings suggest that the use of BS does not automatically improve results. Only if the technique complements the strategy does the technique have a positive impact on ﬁnancial performance. The majority of members of the Institute of Management Accountants (IMA) use BS and obtain improvements in operational performance, while those that do not improve operational performance tend not to use BS. Despite this, many applications of this system fail (DeBusk & Grabtree, 2006).
With regard to the ABC system, the various studies consulted analyse the effects of using ABC on ﬁnancial performance using different methodologies and ﬁnancial performance indicators. ABC has been found to improve ﬁrms’ relative proﬁtability in terms of both accounting and market-based measures (Jänkälä & Silvola, 2012; Kennedy & Afﬂeck-Graves, 2001; Raﬁq & Garg, 2002). Another study ﬁnds a positive association between ROI (return on investment) and ABC, and that synergies exist between ABC and other management techniques such as JIT and TQM (Cagwin & Bouwman, 2002). In contrast, other studies ﬁnd no association between the use of ABC and ﬁrm performance (Gordon & Silvester, 1999; Innes & Mitchell, 1995; Ittner, Lanen, & Larcker, 2002). Firms have used ABC now for more than 20 years, but the literature has still failed to ﬁnd sufﬁcient empirical evidence that adopting the system has an effect on ﬁnancial performance (Gosselin, 2006).
The above discussion means that the productivity paradox remains unresolved. According to this paradox, despite the massive investment in new IS, researchers have still failed to demonstrate a consistent correlation between this investment and productivity (Brynjolfsson & Hitt, 1996). The current study offers an empirical contribution that analyses this correlation, highlighting the role it plays in the success of IS, taking into account the strategic approach adopted, and the quality and implementation of NMTs.
We carried out an empirical study based on a questionnaire sent to a sample of Spanish ﬁrms to try to respond to the questions raised in the introduction. Based on this information certain hypotheses may be drawn and are presented below.
It can be said that the main aim of an IS is to improve and enhance the overall performance of the organisation (Ranganathan & Kannabiran, 2004); this is the reason why this criterion is used to evaluate the IS in this study.
The measure of IS user satisfaction provides a useful assessment of the system’s success (DeLone & McLean, 1992; Escobar Pérez & Vélez Elorza, 1997; Raymond, 1987). The degree of IS utility perceived by users is similar to the expectations of future bene- ﬁts to be realised by using the system (Rai et al., 2002). Users are likely to be satisﬁed with their ﬁrm’s IS and rate it highly when they feel the system will help them improve their decisions and consequently improve the ﬁrm’s ﬁnancial performance. Thus, a positive relationship conceivably exists between the score managers give to their IS and the ﬁrm’s ﬁnancial performance. Obviously, the users of the information obtained with an IS will rate it highly when they are satisﬁed with the system. DeLone and McLean’s IS success model (DeLone & McLean, 2003) is the method most widely used by researchers, both at theoretical and empirical levels (Dörr et al., 2013); this model, as has been previously explained, establishes, among others, the user satisfaction and net beneﬁts variables in order to evaluate the IS. Following this, Halawai, McCarthy, and Aronson (2007) ﬁnd a relation between user satisfaction and knowledge management systems success. However, IS success does not always imply a signiﬁcant improvement of the ﬁrm’s performance (Lee, 2012).
Bearing all this in mind, in order to clarify whether a relationship exists between user satisfaction (measured by the user’s evaluation of IS quality and strategy) and performance, the ﬁrst hypothesis is as follows:
H1. Information systems with high scores are positively associated with the ﬁrms’ ﬁnancial performance improvement.
The possible effect of ﬁrms’ IS on ﬁnancial performance can be evaluated in two ways: ﬁrst, by studying the change in the ﬁnancial performance over a period of time; and second, by examining the ﬁnancial performance observed at a particular moment in time. As the issues covered in the survey refer to the characteristics and the implementation of the IS during the last analysed period, we will be studying the effect the IS has on the improvement of the ﬁrm’s performance.
Since the valuation given by the CFOs regarding information systems is summarised in two main factors, IS strategy and IS quality, Hypothesis 1 has been augmented with two additional hypotheses: H1.1 and H1.2.
“IS strategy alignment is assumed to facilitate the performance of all organisations, regardless of type or business strategy” (Chan, Sabherwal, & Thatcher, 2006: 27). Some empirical studies have found IS strategy alignment to inﬂuence the ﬁrm’s ﬁnancial performance (Chan, Huff, Barclay, & Copeland, 1997; Chan et al., 2006; Jarvenpaa & Ives, 1993; Teo & Ang, 1999). Thus, we propose the following hypothesis:
H1.1. IS strategy is positively associated with performance improvement
Theoretically, it seems fairly clear that quality information may improve ﬁnancial performance, given that this information should allow better management decisions to be made, which may in turn result in improved ﬁnancial performance. Some researchers have found a positive correlation between IS quality and improvement in performance (Byrd, Thrasher, Lang, & Davidson, 2006; Xing- qiang & Ze-jiang, 2009). Byrd et al. (2006) ﬁnd that an IS quality plan is essential for the success of an IS, particularly since the plan improves the quality of the IT system. Consequently, the following hypothesis is presented:
H1.2. IS quality is positively associated with performance improvement.
The literature review suggests the possibility of a positive relationship between the use of NMTs and ﬁnancial performance. The adoption of recent management accounting changes are growing due to their contribution to overall performance of organisations (Adam & Fred, 2008; Vera-Mun˜oz, Shackell, & Buelner, 2007).
Organisations have increased their investments in IS signiﬁcantly with the expectation that these investments will improve the ﬁrm’s ﬁnancial performance (Ravichandran & Lertwongsatien, 2005). Top managers use new management accounting systems or techniques when they believe that they will improve the ﬁrm’s ﬁnancial performance (Abernethy & Bouwens, 2005). There are many empirical studies that analyse the effect of using a NMT on ﬁnancial performance, but few studies have been done considering several of these techniques simultaneously (Kannan & Tan, 2005; Feridun, Korhan et al., 2005; Al-Khadash & Feridun, 2006; Cua, McKone, & Schroeder, 2001). Therefore, more research is needed in this line of study. Consequently, we advance the following hypothesis for testing:
H2. The use of NMT has a positive effect on ﬁrms’ ﬁnancial performance improvement.
Before testing the hypotheses, we ran a principal components analysis1 and obtained three factors relating to the management IS in the sample ﬁrms (use of cost systems, IS quality and IS strategy). The variables used to form the factors were obtained from Likert- type questions in a questionnaire sent to the CFOs of the sample ﬁrms.
From the factors identiﬁed, which deﬁne and evaluate the management IS, we ran a cluster analysis. This led to three types of ﬁrm differentiated by the scores given to their management IS.
We tested the ﬁrst hypothesis by studying the evolution of the ﬁnancial performance variables in the period analysed (1996–2004), using the non-parametric Kruskal–Wallis test, the non-parametric Mann–Whitney test and partial least squares (PLS). The Kruskal–Wallis analysis is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing more than two samples that are independent or not related. When the Kruskal–Wallis test produces signiﬁcant results, then at least one of the samples is different from the other samples. The Mann–Whitney test is useful for analysing the speciﬁc sample pairs for signiﬁcant differences.
Through PLS, which is a technique based on structural equations that allows the building of models with complex relationships between observable and latent variables, a model was con- structed to analyse the effects of IS quality and IS strategy in the improvement of corporate performance. PLS path modelling is recommended in the early stage of theoretical development in order to test and validate exploratory models, being particularly suitable for prediction-oriented research (Henseler, Ringle, & Sinkovics, 2009). Pearson’s chi-square (32) test is used to determine the association or independence of two qualitative variables, such as those related to cluster membership and the use or not of a particular management tool.
To test Hypothesis 2, we use the logistic regression technique, which allows the identiﬁcation of characteristics that differentiate the companies that have improved their ﬁnancial results. Among the variables that explain the improved economic results are the factors deﬁning the IS and use of NMTs.
Using information from the SABI database, from the ﬁrm Informa, which holds accountancy data on Spanish companies, we chose 450 ﬁrms as the object of analysis. The ﬁrms complied with the following requisites:
- Spanish for-proﬁt ﬁrms, operating, and founded before
- Revenues from ordinary activities exceeding D 10 million in
During 2006, we contacted the CFOs of the ﬁrms by phone to inform them of the objectives of the study and request their participation.2 The questionnaire was sent by e-mail to those CFOs who agreed to receive the survey. The questionnaire was sent again to ﬁrms that had not initially responded. Eventually, 56 valid responses were received, which represent a response rate of 12.4%.
The 56 respondent ﬁrms are distributed by sector as follows:
- Industry: 75%
- Commerce: 7%
- Services: 3%.
Table 1 reports on the mean values and the 25th and 75th per- centiles for some of the variables in the sample.
To test for non-response bias, we compared by sectors the responding and non-responding ﬁrms’ revenue, total assets, number of workers and the ratio of operational proﬁt divided by total assets in 2004. There were no signiﬁcant differences across these variables (at p = 0.05) with the exception of revenue, that has a lower value in the case of non-respondents in the industry (D 35,091,900 vs. D 40,994,870). It was then understood that there were no fundamental differences between respondents and non-respondents.
Of the 56 ﬁrms analysed, 58.9% apply at least one NMT (see Table 2). The following are the most widely used: BS (35.7%), TQM (35.7%), and ABC (17.9%).
The dependent variables are ratios to facilitate comparison between the ﬁrms. They are all based on objective data from ﬁrms’ balance sheets, not on the respondents’ opinions. They all measure ﬁnancial performance, and are as follows:
- MARGIN 1. Resources generated by ordinary activities over revenue from ordinary activities: ratio of operational proﬁt plus depreciation to revenue from ordinary
- MARGIN 2. Operational proﬁt over revenue from ordinary activities: ratio of operational proﬁt to revenue from ordinary
- ROI 1. Operational proﬁt over total assets: ratio of operational proﬁt from proﬁt and loss account to total assets from balance
- ROI 2. Proﬁt from ordinary activities over total assets: ratio of operational proﬁt plus ﬁnancial proﬁt (less ﬁnancial costs) to total assets from balance
- ROI 3. Operational proﬁt over operational assets: ratio of operational proﬁt from proﬁt and loss account to total assets from balance sheet less ﬁnancial
- ROI HC. ROI of human capital: operational proﬁt before subtracting labour costs divided by labour
- COSTS/OI. Operating costs over ordinary
To study the change in the results, we chose the period 1996–2004. The reason for this relatively long time period is that it conceivably takes time for the effects of changes in the IS on the ﬁnancial performance to become evident. Researchers have found that the beneﬁts of new IS may not become apparent for two or three years (Brynjolfsson, Gurbaxani, & Kambil, 1994). The initial ﬁnancial performance is measured as the mean value of the period 1996–1997, and the ﬁnal ﬁnancial performance as the mean value of the period 2003–2004.
In order to analyse the initial and ﬁnal relative positions and their change in the period 1996–2004 for the proﬁtability variables, we re-calculated these variables dividing their values by the median of the ﬁrm’s sector (Cagwin & Bouwman, 2002). The variables are interpreted as their relative distance from the sector median.3 The change in performance variables over time is calculated as shown in the following formula:
It should be made clear that this expression does not mea- sure changes in proﬁtability of each company in absolute terms, but rather evaluates the relative performance change for the period 1996–2004 by sector position, irrespective of macroeco- nomic developments, since such inﬂuence will be the same for all ﬁrms in the same sector.
In applying the PLS technique, a latent variable is used that measures the change in the ROI from the beginning to the end of the period, and is constructed via the changes in three indicators: ROI 1, ROI 2 and ROI 3.
This work uses two control variables commonly used in similar studies: ﬁrm size and sector.
When the logistic regression is applied, ﬁrm size is measured by a dichotomous variable that equals 0 if the ﬁrm’s revenue from ordinary activities in the ﬁnal year (2004) is below the median and otherwise. Larger ﬁrms have more resources, professionals and management experts to apply new techniques (Finch, 1986).
Various authors use sector as a control variable (Braam & Nijssen, 2004; Cagwin & Bouwman, 2002). Due to the small number of ﬁrms from the commerce and services sectors in the sample, the three initial sectors are re-grouped into industry and non-industry (commerce and services).
In order to identify the main factors underlying the set of Likert-type variables obtained in the questionnaire (from 1 = totally disagree, to 5 = totally agree), we used principal components analysis.
This technique identiﬁed three factors. Table 3 reports the items making up each factor, along with their validation values.
A brief explanation of the questions in each factor follows, along with their source.
F1, Use of cost system. Using information about costs for various management objectives will facilitate management thereof, which in turn should conceivably enhance the managers’ perception of that information and improve the ﬁrm’s ﬁnancial performance. The questions for this factor are adapted from Krumwiede (1998) and Byrd et al. (2006). In the questionnaire, the item “Product costs are adequately assessed to be able to compete in the market” is also valued, however this has not been included within the F1 factor as it negatively affects its validation.
F2, IS quality. Using quality internal information means that the information will be more relevant and timely, which in turn will conceivably enhance the perception of the quality of the information and improve the ﬁrm’s ﬁnancial performance. The questions for this factor are based on Krumwiede (1998) and Byrd et al. (2006). Due to issues relating to the validation of the F2 factor, the item “Operations data are updated in real time” has been omitted.
F3, IS strategy. Given the importance of the strategy for achiev- ing superior ﬁnancial performance, it is useful to measure the importance of the internal information for the implementation and development of the strategy. The questions for this factor are based on Cagwin and Bouwman (2002), and Braam and Nijssen (2004). The item “Timeliness and relevance are more important than accuracy” has not been taken into consideration in the F3 due to validation issues.
Typology of ﬁrms according to their management information system
In order to analyse the heterogeneity in the ﬁrms’ management IS, we produced a typology of the sample ﬁrms using cluster analysis. Cluster analysis is a multivariate technique that classiﬁes observed cases into homogenous groups with respect to some predetermined selection criterion. The cases in each cluster can be considered “similar”, while the different clusters are assumed to be “distinct” from each other (Aldenderfer & Blashﬁeld, 1984; Hair, Anderson, Tatham, & Black, 1999). Researchers argue that cluster analysis can be used to show different combinations of variables that identify the management IS, which is useful for testing the effect of the system on ﬁnancial performance (Chenhall & Langﬁeld- Smith, 1998b).
We used the k-means technique to carry out the cluster analysis, taking as classiﬁcation variables two factors, IS quality and IS strategy, because they indicate the managers’ satisfaction with the use and quality of the management IS. Since there is a strong correlation between the factor F1 (use of cost system) and factor F2 (IS quality), it was decided not to include the ﬁrst one in the production of cluster groups. The cluster analysis resulted in three groups of ﬁrms distinguished by the values of these two dimensions (Table 4). We calculated the mean of the two factors that characterise the management IS for each ﬁrm, and then the mean for each cluster. Based on that value, the groups were labelled: high (26 ﬁrms), medium (13), and low (17). The high group contains ﬁrms with a high value in the two dimensions of the management IS; the low group contains ﬁrms with the worst mean value in IS quality; and the medium group contains ﬁrms with the lowest value in IS strategy. The non-parametric Kruskal–Wallis test for k independent samples shows that statistically signiﬁcant differences exist between the three clusters in the two factors.
This hypothesis postulates that ﬁrms that score their management IS highly achieve superior improvements in their ﬁnancial performance than the rest of the ﬁrms. Table 5 shows the mean change in the relative proﬁtability indicators over the period 1996–2004 for the three groups of ﬁrms identiﬁed.
We used the non-parametric Kruskal–Wallis test for k independent samples to investigate the existence of signiﬁcant differences between the three clusters of ﬁrms in the change in the ﬁnancial performance. The results show that signiﬁcant differences exist in all the ﬁnancial performance variables in favour of the group of ﬁrms giving the highest score to their management IS. The medium group achieves the lowest changes. This may be because these ﬁrms have a low score in terms of their IS strategy.
We also ran a non-parametric Mann–Whitney U test on the clusters taken in pairs. The results show that statistically signiﬁcant differences exist between the high cluster and the low and medium clusters. When comparing the low and medium clusters, only signiﬁcant differences are observed in the change in ROI 1 and ROI 2 against medium group.
In order to analyse the effect that the variables that determine the cluster groups (IS strategy and IS quality) have on improving business performance, the partial least squares (PLS) technique was used. The model also included the ﬁrm size factor.
PLS is a technique based on structural equations that allows the building of models with complex relationships between observable and latent variables. A latent variable is not directly observable; it is a construct made from other variables that theoretically form (formative indicators) or reﬂect (reﬂective indicators) a factor of interest for the study (represented by the latent variable). This technique has been widely used to analyse relationships between variables obtained from survey responses.
The model shows six relationships between factors or con- structs. The factors represented by circles in Fig. 1 are not directly observable variables; they are obtained from indicators that are in turn responses to different questions in the questionnaire (except ROI change and ﬁrm size). The constructs employed and the indicators that comprise them are presented next. We use reﬂective indicators, which implies that the non-observed construct gives rise to observed indicators. The four constructs used in the model are factors F2 (IS quality) and F3 (IS strategy), identiﬁed in the principal component analysis (Table 3), and the following two:
Firm size. Formed by three indicators:
- FS Ln of total assets at end of period.
- FS Ln of sales at end of period.
- FS Ln of number of employees at end of period.
ROI change. The change in ﬁnancial performance throughout the period, integrated by ROI 1 change, ROI 2 change and ROI 3 change. In the annex it may be seen that the requirements ensuring internal consistency (unidimensionality, reliability, convergent validity and discriminant validity) were met. Latent variables can then be used to test the relationships in the model.
The structural model. R2 and Betas
PLS is used to estimate the structural equations with the aid of the SmartPLS software (Ringle, Wende, & Will, 2005), which allows standardised Beta regression coefﬁcients called “path coefﬁcients” to be obtained. These coefﬁcients test whether the proposed hypotheses are supported or not. R2 values measure the amount of variance of the construct that is explained by the model. The results of the estimation are shown in Fig. 1 and Table 6.
The R2 are shown in Fig. 1, within the circles. The R2 of the latent variable to be explained, ROI change, is 0.306. Table 6 shows the standardised path coefﬁcients (these are also on the lines connecting the constructs in Fig. 1) and the Student’s t values (obtained with a bootstrapping procedure with 500 samples).
The relationship between ﬁrm size and ROI change
According to several studies, increased ﬁrm size can help improve ﬁnancial performance for a number of reasons: larger ﬁrms are more able to take advantage of economies of scale, regarding operating costs and the costs of innovation (Hardwick, 1997), while greater size means the possibility of more diversiﬁcation of activities, allowing ﬁrms to cope more successfully with possible market changes, as well as with high risk situations (Goddard, Tavakoli, & Wilson, 2005; Winter, 1994). However, the conclusions of the various studies do not coincide in this respect, and researchers have yet to establish a clear relationship between proﬁtability and size. González Pérez, Rodríguez, and Acosta Molina (2002) provide a review of the various Spanish studies grouped according to their conclusion regarding the relationship: positive, negative, or non-existent.
Relationship between ﬁrm size and IS strategy
The large ﬁrms are generally more complex and require more formalised, decentralised, specialised and integrated information systems (Mintzbert, 1979). These systems provide the ﬁrms with a greater degree of functional and organisational structure and coordination that aids in effective managerial decision-making (Hendricks, Hora, Menor, & Wiedman, 2012).
Relationship between IS strategy and IS quality
In order to properly serve its purpose, IS strategy needs to be based on quality information. In fact, an IS strategy may be said to implicitly entail a quality IS, because otherwise it would hardly be strategic. Kearns and Sabherwal (2006) found business information technology strategic alignment to be positively associated with quality information technology.
Relationship between ﬁrm size and IS quality
Large ﬁrms are organised in more complex ways, such that they are forced to use more sophisticated and better quality information systems in order to be able to meet their greater coordination and management needs. The ﬁrm size is an essential factor affecting the effectiveness of an IS (Mahmood, Hall, & Swanberg, 2001). IS satisfaction is greater in organisations that are large because smaller organisations tend to be less mature (Lees, 1987).
There are three non-signiﬁcant path coefﬁcients, namely those measuring the relationship between IS strategy and IS quality, the relationship between ﬁrm size and IS quality and the relationship between ﬁrm size and IS strategy.
As the remaining path coefﬁcients are signiﬁcant, the model results indicate that:
- IS strategy has a positive effect on ROI
- IS quality positively affects ROI
- In the analysed sample, size negatively affects the ROI
The results found with the PLS technique show that the IS strategic approach is the most striking factor in improving the business’ performance, which was previously mentioned when interpreting the difference in results between the high and medium clusters.
In order to analyse the effect of sector variations, a multigroup analysis should be carried out with the objective of identifying the differences in the proposed PLS model results between the two sectors that have been identiﬁed: industrial and non- industrial (services and commerce). Given the small sample of non-industrial ﬁrms (14), the multigroup analysis has been omit- ted. The PLS model has been replicated for the industrial ﬁrms subset (42 ﬁrms) and the results are similar to those obtained for the total sample, though it must be pointed out that the IS strategy
- IS quality relation is found to be signiﬁcant, while the IS strategy
- ROI change positive effect also
New management techniques and cluster groups
Table 7 allows us to check the level of use of NMTs in the three clusters, as well as the average number of techniques used and the average years of use of these techniques. The chi-square test (32) enables us to identify signiﬁcant differences for the variables expressed as a percentage of use of different techniques, which correspond to the ﬁrst 8 rows of Table 7, while for the last two variables, the non-parametric Kruskal–Wallis test applies.
The results indicate that the ﬁrms from the high cluster use NMTs more than the rest. This result holds both for percentage of ﬁrms using at least one technique and for number of techniques employed per ﬁrm. The ﬁrms from the low cluster use the least number of techniques. In particular, the use of BS and TQM is signiﬁcantly higher in the high cluster than in the other two clusters. In order to consider the ﬁrms’ experience in using NMTs, we calculated the mean number of years each technique had been used in each ﬁrm, and then the mean for each cluster. But the results show no statistically signiﬁcant differences among the three clusters in this variable.
Hypothesis 2 tests whether a positive relationship exists between use of NMTs and ﬁnancial performance change. In view of the previous results, if the NMTs form part of a management system in which the information has strategic relevance, these techniques can contribute to improved ﬁnancial performance. This is the case of the high cluster.
Using NMTs on their own, without a strategic perspective, may not have a positive effect on ﬁnancial performance. This is what seems to be happening with the medium cluster, which uses NMTs more than the low cluster but has the worst ﬁnancial performance. Most ﬁrms in the high cluster use BS, which seems to be an effective technique for implementing and controlling a strategy that improves ﬁnancial performance. The effect of the IT on ﬁnancial performance is not the same for all ﬁrms. It depends on the strategy chosen, perhaps due to the fact that IT and strategy are complementary in their effect on ﬁrms’ ﬁnancial performance (Shin, 2006). In this line, Chan et al. (2006) ﬁnd empirical evidence that use of IS for strategic purposes has a positive effect on a ﬁrm’s ﬁnancial performance, and Teo and Ang (1999) conclude that using IT for strategic purposes is one of the key success factors in management.
Application of logistic regression
Having found that the margins and proﬁtabilities differ depending on the characteristics of the management IS that ﬁrms use, the task now is to determine if the different dimensions identiﬁed for the IS and the use of NMTs help explain the different margins and proﬁtability change obtained by the ﬁrms. For this analysis, we used logistic regression.
The sample is ranked for each ﬁnancial performance-change variable in increasing order, and the 28 cases with the lowest value are given 0, and the 28 cases with the highest value, 1 (except for operational costs over operational income, where the criterion adopted is the reverse).4 This results in a dichotomous variable for each of the ﬁnancial performance-change variables, which are used as dependent variables in the subsequent logistic regression.
The logistic regression is a conditional probability model for calculating the probability of obtaining each value of a dichotomous dependent variable given a set of predictor variables (Hair et al., 1999).
We divide the sample ﬁrms into two groups depending on the value of a particular variable of performance change: half of the ﬁrms with high values (1) and the other half with low values (0). Thus a functional relation can be established for classifying the sample into each of the two groups. The aim is to identify the characteristics of the IS that serve to characterise the ﬁrms that obtain the best change in the ﬁnancial performance.
Results of logistic regression
The following variables explain the ﬁnancial performance ratio change. With a plus sign, IS strategy, interaction between NMTs (1: use at least one; 0: do not use any) and IS strategy; and with a minus sign, the ﬁrm size variable (Table 8). It should be noted that the effect of the interaction between the NMTs variable and IS strategy would be negative if NMTs were applied and IS strategy had a negative value (little relevance). These results are in line with the results above in the cluster analysis and with what was said about Hypothesis 1.
In this study we have obtained valuations about different aspects of management IS from the CFOs of a sample of Spanish ﬁrms. We ran a cluster analysis which identiﬁed three types of ﬁrm that differ in the scores given to two factors that characterise the IS: IS quality and IS strategy.
The group of ﬁrms with the highest scores in the two dimensions considered obtains the best improvement in relative proﬁtability over the period analysed. At the same time, these ﬁrms also use NMTs more than the rest.
The group of ﬁrms with intermediate scores in the set of two factors is of particular interest since these ﬁrms perform the worst in terms of ﬁnancial results change. This has to do with the fact that the ﬁrms in this cluster have the lowest score in terms of IS strategy.
The PLS model shows the positive effect that IS strategy has on ﬁrm’s ﬁnancial results. The IS quality also has a positive association with improved ﬁnancial results, but the effect of IS strategy is more important and signiﬁcant.
The results from a logistic regression analysis show a positive effect of NMTs on proﬁtability improvement as long as they form part of an IS with a high strategic relevance. These results are in line with those of previous studies (Braam & Nijssen, 2004; Chan et al., 2006; Teo & Ang, 1999).
The results of this study have signiﬁcant implications for companies as investment in new IS and management techniques should be done with strategic direction, aligning said tools with business strategy, which requires a high level of involvement on the part of companies’ managers. The proﬁtability of the IS depends on its utility to manage and improve key strategic areas of the business (Ravichandran & Lertwongsatien, 2005). In this sense, it requires proper planning when designing and investing in IS, in order to ensure their quality and relevance to the development of business strategy (Byrd et al., 2006).
Finally, this work suffers from a number of limitations and there are possible lines of development that should be considered in future research:
- The sample is small, so the conclusions should be taken with
- Future research should include other variables not available through the questionnaire used here, and which conceivably affect the ﬁrm’s internal management system, such as: level of support of top management; resistance to change among users; employees’ educational background; and perceived need for more-sophisticated management IS among managers and management Additionally, given that companies are in a dynamic environment, studies are needed to collect the effects of new variables of IS and their evolution.
- The work refers to a time period (1996–2004) prior to the current economic It would be of great interest to carry out an investigation for the period immediately afterwards until present, in order to know how the different variables that make up the IS affect the ﬁrms’ performance.