Linking Competitor Intelligence with Strategic Decision-Making (SDM)

Transcription

1 Linking Competitor Intelligence with Strategic Decision-Making (SDM) By Sandra Schlick Abstract This research investigates the link between Competitor Intelligence and Strategic Decision- Making (SDM). To this aim, a CIP is introduced to provide a structured approach to analyse the information processes from a broad scope of internal and external sources, and a wide range from raw data until processed intelligence feeding into SDM. To understand this process the CIP was employed to review the literature and theory. The aim is to demonstrate whether an increase in efficiency and effectiveness of the management of the CIP improves SDM. A systems view is adopted to describe the CIP. A mixed methodology approach is adopted, using interviews, questionnaire using Likert scales and two-valued response options, quantitative static, and dynamic models to analyse primary and secondary data. Key words: Competitive Intelligence, Critical Intelligence Portals, Decision Support System, Strategic Decision-Making, Mathematical Modelling System. 1

2 Introduction This research investigates the link between Competitor Intelligence and Strategic Decision- Making (SDM). To this aim, a CIP is used to provide a structured approach to analyse the information processes from a broad scope of internal and external sources, and a wide range from raw data until processed intelligence feeding into SDM. Therefore, this research investigates in how far the CIP approach supports SDM. It further investigates, if such an approach affects the work of the Competitive Intelligence practitioners, and it aims to show that such an approach improves firm performance. The relevance of the systems is checked within a case of the telecom sector. This is further generalised for the census of the telecoms in one country. The first section gives an overview of the research describing the stages of the research. The following four sections examine each of the stages of the research. It examines literature using the CIP approach, discusses an application within one telecom case, outlines future work to investigate the census of the telecoms, and sketches future work to give recommendations to firms. The last section evaluates shortly the internal and external validity per stage of the research. Overview of Research A mixed method approach fulfilled the need of analysing qualitative and quantitative data (Creswell 2003), or just using different methods in the coarse of a study (Trochim 2001). The research design followed a sequential procedure path. The research was carried out in 4 Stages as shown in Table 1. Table 1: Overview of Research stages Issue Strategy Data sources Stage 1 Stage 2 Stage 3 Stage 4 Effect of Application of Recommendat competitor actions CIP ions for CIP through time Connection of Competitive Intelligence with SDM Literature review, Case study Interview Secondary data analysis Data bases, Internet, Data request Primary data Synthesis and analysis optimization. Questionnaire Results from Stages 1 to 3 Within stage 1 published sources were examined using the CIP approach, as reports from Telecom enterprises, professional reports, academic journals, statistical computing literature and procedures, and the scope for a secondary data collection was evaluated. Within stage 2 a case study was chosen to find out the effect of competitor actions. A secondary data analysis and a model construction followed. Through data analysis, the dependencies and correlation of variables (profit, new entrants, patents) were identified. Within stage 3 of future work a primary data collection will be undertaken and restricted to the MNE telecom branch to receive results of a comparable technical standard, to find out to 2

3 the efficiency of the CIP within the firms. Within stage 4 of future work recommendations for the CIP will be elaborated. Research Stage 1: Literature structured in Critical Intelligence Portals (CIP) The Critical Intelligence Portals (CIP) approach to competitive intelligence management was developed by Wright (2005) as a means of illustrating and identifying the flow of intelligence within an organisation. This was later integrated with the work of Schlick (2006), and the resultant structure was adopted, further enhanced and elaborated as a framework with which to organise the literature review for this study. The integrated CIP framework is shown in Figure 1 and it can be seen that it consists of building blocks. Gathered information is processed and structured within the information systems or management blocks. The processed intelligence is then analysed within the decision support block, with the aim to prepare recommendations for strategic decision-making. In this research CI was used as a generic term to cover the activities undertaken to achieve the objectives as set out in the definition suggested by (Jaworski and Wee 1992, p. 23): identifying the intelligence requirements of a company, systematically collecting relevant information on competitors, and processing data into actionable knowledge about competitors strategic capabilities, position, performance, and intentions. Figure 1: Critical Intelligence Portal (CIP) Approach 3

4 CIP Role For this research the system view embedded within the CIP approach was adopted focusing at the interaction, links, and overlaps of the building blocks. This stood in contrast to the module view of CI (planning and focus, collection, analysis, communication, and decision) (Prescott 2003), and (Dishman and Calof 2008). Purpose of the CIP building blocks The CIP consists of a gathering block as databases, Internet, file folders, etc accessible to the information systems. The information systems are: Competitor Information System (CIS), Management Information System (MIS), Mathematical Modelling System (MMS), and Knowledge Management (KM). These building blocks feed information into Decision Support System (DSS) processing information into intelligence for SDM through analytical activities. CIS supplies, and transforms data (Fleisher and Bensoussan 2007) similar to other information systems for other management information purposes such as Strategic Information Systems (SIS), and Executive Information Systems (EIS), and manages data of competitors (Rajaniemi 2004). CIS identifies competitor strategies and tactics (Fletcher and Donaghy 1993), (Sauter and Free 2005). It feeds the DSS with expected strategies and tactics of competitors. MIS monitors internal information; the users are managers and executives. The output consists of predefined periodic reports, and the operations are to summarise information or existing data. Within the example of a sales report for an airline, a MIS can provide frequently updated ticket sales information enabling executives to make management decisions faster (Watson et al. 2006). It feeds the DSS with summarised internal existing and planned capability reports. MMS provides computations to transform the acquired data. It feeds the DSS with outputs ready for interpretations and choices. Some examples illustrate this further. MMS computes upper and lower bonds within scenario analysis (Fleisher and Bensoussan 2007), identifies optimal strategic movements within a game theory board (Nash 1951), and analyses dynamic sets of internal and/or external data within time series models (Lütkepohl 2005). KM organises existing internal information companywide. It is principally concerned with the interrogation of known material in the firm. Swan (2001) claimed that the ability to organise knowledge within KM enables firms to encourage their innovations. It feeds the DSS with organised internal data. DSS analyses and links the information and if necessary feeds back into one of the building blocks. This can be for example the request to gather additional information or to transform data. When accomplished, the DSS feeds the processed and analysed intelligence together with resulting recommendations to decision-makers ready for SDM. The DSS evolves within a knowledge generic engineering cycle as identified by Carson et al. (1998, p. 80). This cycle consisted of human expert, knowledge acquisition, knowledge representation, feeding into the evolving DSS, which closed the cycle when feeding back to the human expert. Additionally a control (inference mechanism) was identified. This corresponded to one of the basic assumptions of this research claiming the evolution of a DSS 4

5 being core and that the DSS would not have to be a technologically sophisticated system, but have a feedback from management to the experts and within the building blocks. SDM is the process of finding managerial decisions. It concerns risky issues, and involves various entrepreneurial functions within different academic disciplines (Papadakis and Barwise 1997). Research Stage 2: Case Study A Swiss MNE telecom firm, Swisscom Solutions (SL) was investigated. Swisscom identified the following analytical methods and sources for information about competitors and innovation: market research and market information, strategic analysis of competitors, coordination of information providers, observation of technological trends, customer data, customer satisfaction analysis of potential, general information about competitors, development of market, product specific observation of competitors (products, and prices), portfolio management, information about competition from customers, and information about competitors based on bids. These sources were partly embedded in the Internet, for fee databases, software supported systems, paper file folders within the business departments, and through communication between and within the departments. Swisscom had several partly independent CIS in various Business-Units. The structure of the various CIS was not standardized. This was the reason they were not used for inter-unit purposes. There was a lack of communication between units, which could be ameliorated with a new standardized CIS. The process of gaining and operating information was not standardized. Communication between Business-Units was not structured (not regular and not observing principles). The aim of the SL CIS project was to exchange information more efficient and effectively between Business-Units with standardised structures of information and to use one CIS also for inter-unit purposes after a phase of realising, and learning (Schlick 2006). Based on the results from the investigation of SL, and as a result from Stage 1 of this research, a secondary data analysis was conducted to find out if competitor actions had an effect on the results of enterprises. The secondary data analysis was carried out with the profit data from Swisscom, patent data from Switzerland, and number of new entrants in Switzerland with annual data during the years 1990 to The results of a times series analysis using a VAR Model showed instantaneous causality coming from the variables Patents and New Entrants to the variable Profit of Swisscom, which means that there is a correlation between Patents, New Entrants and Profit of Swisscom. A tendency of Granger causality of the Patents to Profit was identified, which means that Patents are one variable to cause the profit of Swisscom. Research Stage 3 Future Work: Primary Data Analysis The results from stage 2 demonstrated that there is a correlation between the analysed variables. Therefore it makes sense to investigate if this can be generalised. A primary data analysis further makes sense because no study exists, which has elaborated on the issue to investigate the efficiency of the use of the CIP within firms. Based on the indications from Stage 1 and the results from Stage 2 it is suspected that the standards of the CIP of the firms vary with their performance. 5

6 Sample Frame Selection and Justification The census of the UK MNE telecom firms was identified. The population size was 162 MNEs in June The expected response rate is about 20-30% from postal questionnaires. It is estimated that profit and the efficiency of the CIP have a significant positive correlation (r > 0.25). The questionnaire will use forced choice and Likert scales, both apt for coding into dummy variables. Statistical techniques will be employed to analyse the data. Suitable contacts and addresses have been identified. Anonymity will be offered to respondents. The results will provide the efficiency of the CIP of the UK telecoms. Research Stage 4 Future Work: Recommendations to firms Recommendations will be given for a holistic DSS and a CIP, if the findings from stage 3 of this research confirm the findings from stages 1 and 2. Internal and External Validity per Stage of Research The internal and external validity was evaluated using the approach of (Trochim 2001) and evaluating validity based on the different approaches per stage (Wright and Calof 2006). Internal Validity per stage Stage 1 was accurate as indepth investigation is undertaken. Stage 2 was limited as just 16 years (16 data points) could be received. Stage 3 was accurate as the status of the systems and their interaction of the telecom will be investigated. External Validity per stage Stage 1 was only limited as one case was studied. Cases differ as they depend on technological, administrative, and personal traits (Wright and Calof 2006). As an MNE was investigated with all the required elements on a qualitative basis it was considered as representative for the census in question consisting of MNEs. Stage 2 was accurate considering the dependencies and the model (patent data hold true for other places as European patents were chosen, new entrant data would change with each country, Swisscom is considered as representative for telecom MNEs). If the dependencies were internal of high validity, it would hold true also for other sectors. This holds true only on a limited basis due to the traits (for example technological, personal, and cultural). Regarding other times, the external validity is doubtable, as technological progress growth direction is positive and accelerating, not regular, and not predictable for the far future. Therefore after stage 2 the results are still limited but indicative to existing tendencies. Stage 3 was accurate, as the whole census of the telecom sector will be investigated. It hold true for other telecom sectors (Canada, Switzerland, USA) with variants of the traits. Therefore after stage 3 the results are accurate. 6

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