Assessing the Impact of Market-Focused and Price ... - Springer Link

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Journal of Market-Focused Management, 5, 219 – 238, 2002 2003 Kluwer Academic Publishers. Manufactured in The Netherlands.

Assessing the Impact of Market-Focused and Price-Based Strategies on Performance: A Neural Network Typology PAUL A. PHILLIPS [email protected] Charles Forte Chair of Hotel Management, School of Management, University of Surrey, Guildford, Surrey GU2 7XH, England, UK F. DAVIES [email protected] Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff CF10 3EU, Wales, UK L. MOUTINHO [email protected] Foundation Chair of Marketing, University of Glasgow, Department of Business and Management, Glasgow G12 8QQ, Scotland, UK

Abstract Marketing practitioners operate in a turbulent environment with increased market competition and more discerning customers, which make it a necessity for organisations to constantly re-appraise their competitive strategies. Porter’s (1980) typology is one of the most widely cited by academics and practitioners, but it is debatable whether a single strategy will lead to sustainable competitive advantage (Helms, Dibrell and Wright, 1997). Using a systems perspective where the focus is on the interaction between dependent and independent variables, this study shows how SBU managers could use neural networks to help improve the strategy formulation process in the hospitality sector. Findings suggest that market-focused and price-based strategies have contrasting effects upon performance. This study extends the knowledge of strategy formulation and performance by focusing on the service industry, and provides controls for market-level influences by being restricted to the hotel sector. Keywords: market-focused, price-based strategies, performance and hotels

Introduction The strategy literature has advocated a variety of universal rules and concepts to enhance performance. However, there are several inconsistencies in the literature. Porter’s (1980) typology is one of the most widely cited by academics and practitioners, but there is growing cognizance that no single strategy will lead to sustainable competitive advantage (Helms, Dibrell and Wright, 1997). Turbulence in the external environment, market competition and more discerning customers have made it a necessity that organisations constantly re-appraise their competitive strategies. Therefore, if organisations are to obtain

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sustainable competitive advantage, then they need to understand the linkages among strategy, strategic planning and performance at the corporate and strategic business unit (SBU) level. This paper shows how SBU managers (middle managers) could use neural networks to help improve the strategy formulation process in the hospitality sector. It first reviews the contributions from the strategy-performance and market orientation-performance literatures and neural networks, then describes the neural network approach to data analysis. The research model, which includes strategy, strategic planning and performance, is described. Data analysis, implications and conclusions are discussed.

Strategy and Performance The strategy literature is replete with strategy topologies, research methodologies and theories on the strategy-performance relationship. Turbulence in the external environment, market competition and more discerning customers have made it a necessity that organisations constantly re-appraise their competitive strategies. However, despite nearly two decades of empirical research, key questions remain. For example, Porter (1980) suggests that to ensure long-term profitability, the firm must make a choice between one of his generic strategies, rather than end up being ‘‘stuck in the middle’’. His assertions have been supported by several studies (Dess and Davis, 1984; Hambrick, 1983; Nayyar, 1993; Parker and Helms, 1992; Reitsperger, Daniel, Tallman, Parker and Chismar, 1993). However, several studies have suggested that in higher performing businesses, low cost and differentiation strategy may be adopted simultaneously (Buzzell and Gale, 1987; Gupta, 1995; Hall, 1983; Slocum, McGill and Lei, 1994). In fact, Sharp (1991) suggests that having a cost advantage is merely a facilitator to differentiate, usually on price. Miller (1992) feels that a mixed strategy, combining some aspects of differentiation with cost effectiveness, has advantages. These benefits are due to the over-specialisation while allowing organisations to develop multiple and synergistic benefits. In an attempt to investigate whether low cost and differentiation are mutually exclusive or whether they can be adopted simultaneously, Helms et al. (1997) found that business units that simultaneously compete on low cost and differentiation strategies have higher ROI. While cost leadership and differentiation are about how to compete, focus is about where to compete (Partridge and Perren, 1994). According to Partridge and Perren, it is unwise for the lowest-cost competitor to target on a narrow market, since it needs to maximise revenue. Focus strategies are better suited for differentiators, who seek to offer a product/ service that might fulfil the need of a targeted segment.

Market Orientation and Performance The current proliferation of studies on the relationship between market orientation and performance (e.g. Chang and Chen, 1998; Caruana, Pitt and Berthon, 1998; Deshpande, Farley and Webster, 1993; Gray, Matear, Boshoff and Matheson, 1998; Greenley, 1995a,b;

THE IMPACT OF MARKET-FOCUSED AND PRICE-BASED STRATEGIES

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Jaworksi and Kohli, 1993; Kumar, Subramanian and Yauger, 1998; Ruekert, 1992) mean that considerable effort has been devoted to evaluating the impact of key behavioural aspects of marketing (customers and competitors). Indeed, Hooley, Broderick and Moller (1998) argue that perhaps market orientation research has reached its zenith and it is not clear what further replications of such studies may add. It is hypothesised that more market-oriented firms seem to enjoy a higher level of business performance (Chang and Chen, 1998; Jaworski and Kohli, 1993; Ruekert, 1992), which may produce superior quality, enhanced productivity and stronger customer loyalty (Zeithaml, Parasuraman and Berry 1990). However, there is still some equivocality over the market orientation-performance relationship (Greenley 1995a; Caruana et al., 1998). Matters are not helped by a confusion of terminology (Gray et al., 1998). For example, marketing orientation (business philosophy) and market orientation (implementation of that philosophy), and customer-led (satisfying customer expressed needs) and marketoriented (satisfying customer latent needs) illustrate the need for precision. As a consequence of this confusion, existing research has not yet been able to construct a valid and reliable measure of market orientation (Kumar, Subramanian and Yauger 1998; Gray et al., 1998). In their conclusion Kumar et al. (1998) suggest that researchers may benefit by viewing market orientation as a configurational concept and then ascertaining how the differences in forms of market orientation affect performance. Gray et al. (1998), citing Greenley (1995b), conclude that there may be different modes of market orientation, whereby different combinations of customers and competitor orientation, interfunctional co-ordination, responsiveness and profit emphasis levels may produce similar benefits. Another approach has been to focus on the individual variables of strategic marketing planning. Therefore, models that allow us to predict the effect of alternative types of strategic marketing planning on performance are helpful.

Strategy Formulation at the Strategic Business Unit Level (SBU) Ansoff (1960) was among the first to conceptualise different levels of organisational decision-making. Ansoff made references to three levels: strategic decisions – the selection of product mix and markets, an impedance match between the firm and the environment; administrative decisions – structuring a firm’s resources to maximise performance potential; and operating decisions – maximising the efficiency of the firm’s resource conversions process (p 5– 6). Despite Ansoff’s observation four decades ago, the amount of strategy research looking at SBU and industry-level sources of competitive advantage in comparison remains modest. The development of middle managers should be a key concern to top management, as their performance is ultimately related to organisational effectiveness. Several authors have discussed the significant role that middle managers play during the strategic planning process. Bower (1970) posits that middle managers are best able to decide whether strategic issues are being considered in the proper context. There is also some evidence of a relationship between middle manager involvement and organisational performance.

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A study by Wooldridge and Floyd (1990) of 20 organisations and 157 managers, showed a statistically significant relationship between middle management involvement in strategy and organisational performance. The influence and importance of middle managers in strategy formulation has been highlighted in Burgelman (1994), who performed a comparative study of Intel Corporation’s strategic position in two semi-conductor businesses. During a time of shrinking market for its dynamic random access memory (DRAM), middle managers diverted resources away from the core DRAM business to new, more profitable opportunities in the microcomputer business. This change in strategic direction took place not because of a change in grand strategy from top management, but due to the fact that top management had evolved an internal selection environment that was more robust. Where middle managers are strategically involved in the planning process, they can help sharpen and in some cases change the strategy developed by top management (Tregoe and Tobia, 1990). Another study concluded that top management must acknowledge that the role of the middle manager has unalterably changed from a technocrat to a knowledge-based individual who is asked to do more with less (Thakur, 1998). Phillips (1996) speculates that there are three strategy levels within a typical UK hotel group. At the corporate level; strategic planning, acquisition and mergers, and financial management are used to provide guidance to hotel units. In the case of the major hotel chains, it is becoming increasingly more important to convey appropriate signals to major stockbrokers. At the SBU level, hotel general managers (HGMs), sometimes with support of regional staff, deal with how best to compete in their local market. This implies that the unit level is appropriate for strategic planning, and that the HGM is not merely an operator of a hotel but also a strategist, and manager. Thus, if decentralisation is to work, defining specific strategic planning responsibilities of corporate and SBU managers becomes a necessity. To facilitate implementation, the planning process should include a team of staff drawn from all levels, which is preferable to the HGM working alone with support from HQ (Phillips and Moutinho, 1999). They assert that good upward communication enables staff throughout the hotel organisation to enjoy ownership of the strategic plan, and make them more likely to be committed to carrying it out.

Use of Neural Networks in Business and Marketing Although neural networks have been in use for many years in other fields of study, their use is a fairly recent development in the business world (see Appendix A for background to neural networks). As researchers realise their flexibility and the wide range of problems for which they could be used, more and more applications are being heard of. Several authors have investigated the performance of neural networks compared to other more traditional statistical techniques, such as regression analysis, discriminate analysis, and cluster analysis. Venugopal and Baets (1994) have carried out a conceptual comparison of NNs with these techniques. Broadly, applications may be divided into the two categories of forecasting and classification. In the domain of market forecasting, NNs have been used by Fitzsimons, Khabaza and Shearer (1993) to predict television audiences for the BBC, and by Tedesco (1993) to


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predict sales in ‘‘Speciality Consumer Outlets’’ of Amoco. Poopalasingam and Nellis (1996) found a neural network outperformed a linear approach in predicting propensity of customers to purchase, and they concluded that neural techniques were highly effective when the data contained non-linear characteristics. Researchers in related fields have also tested NN performance in forecasting. Kouam, Badran and Thiria (1992) found that NNs provide good performance on stationary time series forecasting, while De Groot and Wurtz (1991) found NNs outperformed standard models when data exhibited non-linear characteristics. Bansal, Kauffman and Weitz (1993) found NNs to be more robust than regression analysis as data accuracy degraded, Udo (1993) found an NN to compare favourably with a multiple regression model in predicting company bankruptcies, and Hruschka (1993) found an NN model gave a better fit than an econometric market response model. Neural nets have also been found to give better predictions than a regression model of yields of share indices on the stock exchange (Bastaens and Van den Bergh, 1992), and to be better at predicting exchange rates in a time series context than exponential smoothing and autoregression (Refenez, Azena-Barac, Chen and Karoussos, 1993). As regards the use of neural networks for classification problems, various researchers (Huang and Lippman, 1987; Swales and Yoon, 1992; Tam and Kiang, 1992; Subramanian, Hung and Hu, 1993; Hashemi, Le Blanc, Rucks and Shearry, 1995) have found NNs outperforming conventional discriminant analysis in various fields. In the marketing field, NN models have been applied in market segmentation (Mazanec, 1993; Dasqupta, Dispensa and Ghose 1994; Davies, Moutinho and Curry, 1996), while they have also been used to choose the most appropriate sales promotion tool (Kluytmans, Wierenga and Spight, 1993). Thus, the range of NN applications in the business world is continually expanding as researchers and practitioners explore the potential of the approach.

Method For the purposes of this study, the hotel unit was viewed as a strategic business unit [SBU], so it was imperative that the sample consisted of firms that were likely to engage in strategic planning. The sampling frame was derived from Quoted Hotel Companies (Slattery et al, 1994). The sampling frame used for this study was the top 50 UK hotel groups (Hotel and Catering Research Centre, 1993). To qualify for the research the hotel group had to satisfy the following four criteria: i. ii. iii. iv.

UK owned consisted of more than 250 rooms average size of hotel units in excess of 50 rooms hotel business being a significant segment of group turnover.

This led to 17 hotel groups qualifying. A letter was mailed, together with a one page summary of the study, to a senior head office executive to ask if their group would be

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prepared to take part in the research. After several reminders via mail, fax, and telephone, fifteen groups agreed to participate in the study (30 per cent). Reasons for non-response were: ‘‘it is not our policy to participate in surveys’’ or ‘‘regrettably due to constraints upon very limited resources we are unable to assist on this occasion’’. The data used for this paper were collected as part of an on-going research study (Phillips, 1996). A 17-page questionnaire was developed, which sought information on organisational strategy, strategic planning systems characteristics, and business performance at the SBU level. The questionnaire was pretested through structured interviews with academics and practitioners, who were asked a series of closed and open ended questions. An initial letter was mailed to a contact or the Managing Director of each hotel group introducing the researchers, explaining the study, and requesting their hotel general managers’ (HGMs) participation in the study. Fifteen groups agreed to participate in the study (30 per cent). The final questionnaire was then mailed to 130 HGMs and 100 were completed and returned (77 per cent). The average hotel group sales turnover was £18.8 million. The average hotel unit in the sample consisted of 137 rooms with an average sales turnover of £2.82 million. In an attempt to address some of the problems of measuring performance, Walker and Ruekert (1987) stated that the dimensions that are of primary importance to corporate and business unit managers can be broken down into effectiveness, efficiency, and adaptability. Any comparison of business performance with only these three dimensions involves substantial trade-offs; good performance on one dimension often means sacrificing performance on another (Donaldson, 1984). Business performance attributes were measured using a judgmental approach using seven point scales. Respondents were asked how they rated the performance of their hotel operation over the past year in comparison with primary competitors. The study also noted the issue of the time-lag that exists between strategy implementation and the resulting improvement in performance. Respondents were asked how they expected their hotel to perform relative to primary competitors over the next two years. The level of business performance was determined by the following indicators:

Effectiveness Occupancy percentage Average room rate Growth in sales per room Efficiency Return on investment Profit margin Adaptability Number of successful new services/products introduced Percentage of sales accounting for new services/products


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The Research Model Notwithstanding, the paucity of empirical evidence, the hospitality literature asserts that strategic planning produces economic value. Thus, hoteliers who are seriously seeking to uplift demand and business performance are now placing much importance on developing mission statements, key objectives, and strategies in a dynamic external environment. However, given the apparent gap between theory and practice (Phillips, 1994), together with the fact that a good product flows only from a good process (Feltenstein, 1992), it seems relevant that the strategic planning process in hotels should be effective. As little empirical evidence has been available to-date to assist hoteliers in determining the effectiveness of their strategies, this study is both timely and critical to tourism as well as hotel planning. This study adopts a systems perspective where the focus is on the interaction between dependent and independent variables. This study assumes a broad definition of processes to include strategic marketing planning activities and information flows between functions. In addition, we extend the knowledge of strategy-performance and market orientation-performance by focusing on the service industry, which provides controls for market-level influences by being restricted to the hotel sector. The foundation of our model lies in constructs identified by Phillips (1996) during his survey of the literature pertaining to the design of strategic planning systems. Four high level characteristics were found to be significant in strategic planning systems. These were strategic orientation, vertical integration, environmental characteristics and business performance. The 97 variables measured were grouped and then aggregated within groups to produce a set of 22 input variables (see Table 1), each measuring a different construct. Constructs were selected to produce a set, which represented the research model (Figure 1). Aggregation of responses was a logical and necessary step as, firstly, in most parts of the questionnaire, several questions were used to measure different aspects of a single construct, and it was therefore sensible to measure the construct as a whole. Secondly, an attempt at factor analysis using all variables individually could not be made to converge. Variables which could not be logically combined were left as single input variables (those listed as VAR2, VAR3, etc. in Table 1). In an attempt to address some of the problems of measuring performance, Walker and Ruekert (1987) stated that the dimensions that are of primary importance to corporate and business unit managers can be broken down into effectiveness, efficiency, and adaptability. Any comparison of business performance with only these three dimensions involves substantial trade-offs; good performance on one dimension often means sacrificing performance on another (Donaldson, 1984). Business performance attributes were measured using a judgmental approach using seven point scales. Respondents were asked how they rated the performance of their hotel operation over the past year in comparison with primary competitors. The study also noted the issue of the time-lag that exists between strategy implementation and the resulting improvement in performance. Respondents were asked how they expected their hotel to perform relative to primary competitors over the next two years. The level of business performance was determined by the following indicators:

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EFFICIENCY (1) assessment of efficiency over past year EFFECTIVENESS (1) assessment of effectiveness over past year ADAPTABILITY (1) assessment of adaptability over past year EFFICIENCY (2) assessment of efficiency over next two years EFFECTIVENESS (2) assessment of effectiveness over next two years ADAPTABILITY (2) assessment of adaptability over next two years

Comparison with Linear Regression Model A linear regression was run for each of the six dependent variables, using the 22 independent variables and the stepwise method of adding dependent variables. In each case only one, two or three of the 22 independent variables were found to contribute significantly to the regression equation, and in each case the adjusted R squared value was found to be less than the R squared value obtained using the neural network. Table 2 shows the comparisons. Thus a linear regression model identifies certain variables which have a linear relationship with the independent variables, the most notable being thoroughness of planning and the perception of a high level of price competition (VAR15). While this is useful, it can be seen that the variables significant in the linear regression model in each case explain noticeably less of the total variance than that explained by the neutral network. The literature review has indicated that we would expect more of the independent variables than those listed in the table, to have an impact on the six dependent variables. So we may infer that other variables have an effect that cannot be modelled by a linear regression model. While there are other multivariate models that we might choose, the literature gives no clues as to what particular model specification may be appropriate. An important distinction between conventional statistical regression methods and the back-propagation neural network (BPNN) model is that regression methods relate independent variables directly to the dependent variable. BPNN models, on the other hand, relate independent variables (the input layer) indirectly to dependent variables (output layer) by establishing a number of hidden nodes. The function of the hidden layer is believed to ‘‘represent the internal structure of input data’’ which, in turn, yields a better approximation in terms of mapping input data onto output patterns. With a sufficient number of hidden nodes, the BPNN model can minimise error and provide a better approximation. The incorporation of hidden layers within the neural network model provides more degrees of freedom and therefore offers substantially greater flexibility in building a complete model of human behaviour. Parameter calibration of a BPNN (Back-propagation neural network) model is conducted through a ‘‘replicative learning’’ process. The process is conducted by iteratively and simultaneously changing the weights of each connection to minimise the error (Euclidean distance) between desired outputs and actual outputs. The learning process is usually a time-consuming process, which ultimately approximates the global (universal) minimum. Therefore, in terms of the development of the neural network topology, the focus has to be placed on developing algorithms which can minimise the global error (i.e. approximating global minimum) and speed up the learning process.

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Table 1. Input variables and measurement. Label VAR2 VAR3 VAR4 VAR5 VAR13 VAR14 VAR15 VAR45 MARCOM FINCOM FORMAL PARTICP SOPHIST THOROUGH PROPLAN NEWPROD NEWCUS HICOMP PROTECH MARUND FINUND FININT

Input variables low price strategy marketing-based differentiation product-based differentiation focussed strategy perception of many ‘‘promotional wars’’ in hotel sector perception that any offer can be easily matched by competitors perception of high level of price competition in hotel sector marketers exert more influence than accountants in long range planning process frequency of communication with members of head office marketing function frequency of communication with members of head office finance function formality of planning level of participation in planning level of sophistication in planning level of thoroughness in planning extent of agreement with theoretical benefits of business planning extent to which new products/services are perceived to be required extent to which catering for new customers is perceived to be important extent to which competition in sector is perceived to be high extent to which technology is perceived as important extent of understanding between general manager and marketing function extent of understanding between general manager and finance function extent of integration between financial and business planning

Measurement 7 7 7 7 5

point point point point point

Likert Likert Likert Likert Likert

scale scale scale scale scale

5 point Likert scale 5 point Likert scale 5 point Likert scale 7 point Likert scales 5 items 7 point Likert scales 5 items 7 7 7 7 5

point point point point point

Likert Likert Likert Likert Likert

scales 6 items scales 5 items scales 19 items scale 7 items scales 14 items

5 point Likert scale 2 items 5 point Likert scale 3 items 5 point Likert scale 5 items 5 point Likert scale 3 items 5 point Likert scale 4 items 5 point Likert scale 6 items 5 point Likert scale 5 items

The BPNN model is an advanced multiple regression analysis neural network model that is capable of dealing with more complex and non-linear data relationships than standard regression analysis. A goodness-of-fit value (R2) was calculated to evaluate the performance of the network model; these R2 values are calculated by comparing the root mean squared (RMS) between desired output and actual output divided by the variance of desired output and are similar to R2 coefficients provided in multiple regression analysis. As explained earlier, one advantage of neural network models is their ability to serve as ‘‘universal approximators’’. Thus, in this case, we have been able to use the neural

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Figure 1. The research model.

network to explain additional variance to that explained by the linear regression model, without the necessity of specifying a functional form in advance, and so saving the time that would be spent in trying out different functional forms. In addition to the above observation, it was decided to use NN for several other reasons. Firstly, the input data was judgemental rather than factual, so there was some ‘‘fuzziness’’ in the data - the numbers used in the analysis were indicators of feelings or perceptions rather than exact observed values. It was more important to look for overall patterns in the data than to try to formulate equations relating inputs to output. Secondly, there was a high degree of correlation between the different inputs - this has no effect on the performance of a neural network or the validity of its results, but needs special consideration if carrying out a multivariate analysis. Finally, the use of a neural network allows the labelling of hidden layer nodes (Davies, Moutinho and Curry, 1996; Mitchell et al., 1999) - thus conjunctions of factors contributing to each hidden node could be examined to see if they indicated an underlying management philosophy which would impact either positively or negatively on performance. A few different networks were tried, and it was found that the optimal fit between inputs and output was achieved with a network with a single hidden layer of 2 nodes. For this network the R2 values, which measure the amount of variance of the output variable explained by the NN model, were 0.31, 0.34, 0.12, 0.32, 0.42 and 0.37 respectively for the six outputs. Therefore, the model explains over 30% of the variance of perceived performance on every output variable apart from ADAPTABILITY (1).


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Table 2. Comparison between neural network and linear regression results. Independent variable

R2 (NN)

R2 (regression)

Dependent variables included in regression

EFFICY1 EFFECT1 ADAPT1 EFFICY2 EFFECT2 ADAPT2

0.31 0.34 0.12 0.32 0.42 0.37

0.16 0.19 0.03 0.21 0.21 0.18

THOROUGH VAR15 THOROUGH PARTICP THOROUGH VAR15 SOPHIST THOROUGH VAR15 PROTECH THOROUGH

It was hypothesised that the model would give insights into the following areas: (1) the input variables which had most impact on performance, as measured by the six output variables (2) the conjunctions of factors which could indicate an underlying philosophy, and its impact on performance (3) the type(s) of strategy best followed by hotel managers wishing to improve performance

Data Analysis Inputs to Hidden Nodes A number of critical impacts deriving from the input measures of the neural network topology can be detected (see Table 3). The salient contributory and inhibitory weights attached to the input variables of the model affecting hidden node 1 demonstrate a more positive impact overall than a negative one. The importance of pursuing a focused strategy by an organisation in the hospitality sector is stressed in the neural network (+1.11). The two most important input factors affecting hidden node 1 are the level of thoroughness in planning (+3.19) and the perception of a high level of price competition (+3.08). The perceived fact by managers that marketers are more influential than accountants in strategic planning is also indicated by a strong contributory weight (+1.80). Also, the frequency of communication with members of head office marketing functions has a strong positive impact on hidden node 1 (+1.25). Finally the extent of integration between financial and business planning is also shown to have a favourable impact on hidden node 1 of the network (+1.23). Some input variables show negative impacts on hidden node 1. For example, the pursuit of a mainly product based differentiation strategy has an inhibitory effect on hidden node 1 (1.80). A surprising finding indicates that the extent of understanding between the general manager and the marketing function has a negative impact on this same hidden node (1.86). Also, the extent to which catering for new customers is perceived to be important has an inhibitory weight which affects hidden node 1 (1.29). Finally, the formality of the planning process also seems to impact in a negative way on hidden node 1

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Table 3. Impacts of input nodes on hidden nodes. INPUT NODE

IMPACT ON HN1

IMPACT ON HN2

VAR2 VAR3 VAR4 VAR5 VAR13 VAR14 VAR15 VAR45 MARCOM FINCOM FORMAL PARTICP SOPHIST THOROUGH PROPLAN NEWPROD NEWCUS HICOMP PROTECH MARUND FINUND FININT

+0.25 0.76 1.80 +1.11 +0.27 0.22 +3.08 +1.80 +1.25 0.42 1.13 +0.30 +0.29 +3.19 0.15 0.72 +1.29 +0.08 0.13 1.86 0.22 +1.23

+1.22 0.47 0.68 +1.42 1.01 0.89 +1.68 +1.48 0.96 +0.96 +0.13 +0.58 2.61 1.28 0.05 0.49 0.36 +1.11 0.92 +0.03 1.05 0.57

of the network topology (1.12). Based on this assessment of all contributory and inhibitory weights derived from the input measures, we have decided to label hidden node 1 as a Market-Based Focused Strategy. With regard to the impact of the input variables on hidden node 2, one can detect that the most salient weights (contributory and inhibitory) are almost evenly split. Also, the intensity of the overall impacts is somewhat lower as compared to the ones affecting hidden node 1. Five input factors are shown to have a positive impact on hidden node 2. The highest positive impact is derived from the perception of a high level of price competition (+1.68). The second highest positive impact on hidden node 2 stems from the adoption of a focused strategy (+1.43). The fact that marketers are perceived as having a more influential role than accountants in long-range planning clearly has a positive effect on node 2 (+1.48). The pursuit of a low price strategy also has a positive impact on the same node (+1.22). The last salient positive impact on hidden node 2 is derived from the extent to which competition in the sector is perceived by the managers to be high (+1.11). Within the input variables which carry inhibitory weights in their effect on hidden node 2, the highest negative impact is derived from the level of sophistication in planning (2.61), followed by the level of thoroughness in planning (1.28). The two last relevant input factors having a negative effect on node 2 are the extent of understanding between the general managers and the Finance Function (1.05) and the perception that many ‘‘promotional wars’’ take place in the


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hotel sector (1.01). After assessing this particular facet of the neural network topology, it was decided to label hidden node 2 as a Price-Based Competition Orientation. Hidden Nodes to Output The most striking conclusion is the fact that hidden node 1 has a positive impact on all the six output measures, whereas hidden node 2 has a negative impact on all the performance variables (see Table 4). Notably, a market based focused strategy greatly affects return on investment and profit margin, both in the past and in the future (EFFICY 1: þ1.50; EFFICY 2: þ1.25). To a lesser extent, the same strategy will also contribute in a positive way to occupancy increase, increase in prices, sales growth, ROI and profit margin in the not-so-distant future (EFFECT 1: þ0.98; EFFECT 2: þ0.71). Although with even smaller levels of impact but nevertheless positive ones, a marketbased focused strategy has played a contributory role during the past year’s company performance in terms of occupancy rate, average room rate, growth in sales per room, return on investment and profit margin (ADAPT 1: þ0.06; ADAPT 2: þ0.37). Finally, with regard to hidden node 2, it was found that a price-based competitive orientation will negatively affect all the output measures in more or less a similar way. The range of negative impacts is quite narrow when differentiating amongst the performance of indicators. Still, the greatest negative impact is on occupancy rate, average room rate and growth in sales (past year) (EFFECT 1: 1.87). There is also a reasonable negative impact on ROI and profit margin in the future (EFFICY 2: 1.51); on the number of new services/products introduced and on the percentage of sales accounting for new services/products (ADAPT 2: 1.70) as well as future occupancy rates, future average room rate and future growth in sales per room (EFFECT 2: 1.50). Finally, the smallest level of negative impact derived from the price-based competitive orientation is found in the two remaining output variables: ADAPT 1: 0.95 and EFFICY 1: 0.81, which means that a price-based orientation has had a negative impact on the number of successful new services/products introduced by companies as well as on the percentage of sales accounting for new services/products, as well as on past occupancy rates, past average prices and on the past growth rate in sales per room.

Table 4. Impacts of hidden nodes on output node. Impact on

HN1

HN2

EFFICIENCY (1) EFFECTIVENESS (1) ADAPTABILITY (1) EFFICIENCY (2) EFFECTIVENESS (2) ADAPTABILITY (2)

+0.98 +1.50 +0.05 +0.71 +1.25 +0.37

1.87 0.81 0.95 1.50 1.51 1.70

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Managerial Implications and Conclusion Our results show that the various strategies have a discriminating effect on performance. The strategic orientations represented by the two hidden nodes have certain similarities, and yet certain key differences, which result in contrasting effects upon performance. Both orientations perceive high price competition, and appear to realise the importance of a focused strategy and of marketing (marketers perceived as more influential than accountants). However, the two nodes represent different ways of responding in a competitive marketplace. Hidden node 1 shows a thoroughly planned, well-integrated, market-focused strategy, making use of available marketing expertise in head office – this good communication and unified marketing throughout a hotel group are likely to produce benefits in the form of a strong brand image for the whole group. The strategy is not product based, nor does price play any significant part. Although planning is thorough, it is not over-formal – thus allowing quick response to changing market conditions. The positive effects on performance, shown for this orientation, corroborate previous findings on the market orientation/business performance relationship (Chang and Chen, 1998; Jaworski and Kohli, 1993; Ruekert, 1992), but also emphasise the importance of thorough yet adaptable strategic planning. In contrast, in hidden node two the strategic focus is on price differentiation – perceptions are that the competition within the industry is price-based rather than promotion-based, and the strategy is to engage in the price wars rather than to find an alternative form of differentiation. Coupled with this, planning is neither thorough nor sophisticated. The negative effects of this orientation on performance measures would back up the assertions made by Partridge and Perren (1994) that low cost differentiation combined with a focused strategy is not a wise proposition. An interesting finding is the relative influence of marketing vis-a`-vis the finance function in both hidden nodes. This is evidence that accountants need to broaden their role from number crunching to that of a value-added business partner. Moreover, given that in the hotel sector a price-based strategy requires a low cost base, there is a window of opportunity for accountants. The relationships from the hidden nodes to output clearly reveal that marketing offers a competitive advantage within the hotel sector. If the hotelier is able to get the correct balance then benefits will accrue across all performance outputs, whereas a price based strategy did not enhance performance and if pursued aggressively could actually destroy shareholder value. Given the influence of marketers in the longrange planning it is perhaps worth asking the question how can other functions (e.g. finance) become more influential. This study suggests five major implications for managers of hospitality firms. 1. Thoroughness in planning pays off when SBUs are pursuing a dynamic and evolving market-based focused strategy. 2. SBUs pursuing a market based strategy are more likely to attain higher positive levels of corporate performance and their strategic planning process is more heavily influenced by marketing managers, as well as having a much better integration between financial and business planning.


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3. Market-based focused strategies should be designed on the basis of marketing-led differential advantages as opposed to a mere product-based differentiation. 4. Too great a degree of formality embedded in the planning process hampers both the pursuit of market-based focused strategies and overall business performance. 5. Market-based focused strategies, as opposed to price-based strategic recipes, are more likely to experience a greater degree of positive impact on a myriad of performance measures related to efficiency, effectiveness and adaptability to competitive environments. This research highlights the problems of conceptualisation and adoption of competitive strategies. The results from HN1 show that a single strategy of a well-planned market-based focus strategy can enhance performance. However, the type of strategy pursued needs to be carefully selected. For example the results from HN2 shows that the adoption of a pricebased competition strategy can adversely affect performance, and a mainly product based differentiation strategy was also shown to have negative impact on HN1. A crucial factor thus seems to be that the strategy should be market-oriented. The primary concern should be to meet the needs of specific markets, and tailor the service, image and positioning to the requirements of chosen, focused segments - rather than being product-oriented or too competitor-oriented (too concerned with undercutting competitors to find out whether customer needs are being met). This of course implies a commitment to market research, and also the necessity of quick response to changing market conditions, which may be why too much formality in planning shows a negative impact on HN1. The success of chains such as Holiday Inn Worldwide, and Marriott, would back up this proposition. Thus effective strategic planning must be based on sound knowledge of the market and the environmental factors impacting upon it, together with an understanding of the organisation and its capabilities so that proposed strategies can be implemented. Quality, rather than the amount of time spent on strategy formulation, is a critical aspect of effective planning.

Appendix A. The Neural Network Approach The origin of the neural network approach is rooted in physiology and psychology, the aim being to work with a direct analogy of the human brain as a set of interconnected processing nodes operating in parallel. Thus the neural network replicates the network of neurons which carry out the lower level computational actions (as opposed to higher level cognitive actions) in the human brain. Learning takes place from examples presented to the network these may consist either of inputs and outputs (‘‘supervised learning’’, corresponding to statistical estimation of dependent variables, or classification into pre-defined sets), or inputs only (‘‘unsupervised learning’’, corresponding to clustering into non-pre-defined groups). This research uses the former method, with the input variables (as listed in Table 1) being the independent variables, and the six output nodes (assessments of past and future efficiency, effectiveness and adaptability) being the dependent variables. The most basic neural network model, the ‘Perceptron’ (Rosenblatt, 1958; Minsky and Papert, 1969) consists of a layer of input nodes, each of which is connected to each node in

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a layer of output nodes. The value calculated by the network for each output node is a weighted sum of the input nodes, the weights being represented by the input-output node connections. The goal of the network is to find values for the connection weights that give the least difference between the actual dependent variable values and those values calculated by the network (as measured by an appropriate error-metric, usually leastsquares). For the Perceptron model, the most common scheme for supervised learning is an iterative procedure known as the Delta rule, whereby the weights for each connection are adjusted in proportion to the difference between the actual output layer values and those values predicted by the network. The Perceptron model was found to have some major limitations, e.g. Minsky and Papert showed it could not deal with a simple ‘XOR’ structure. However, the neural network approach can be rescued from such difficulties by adding one or more hidden layers. These are layer(s) of nodes between the input nodes and the output nodes, such that each layer in the hidden node is connected to each input layer node and each output layer node, with the network calculating values for each node as a weighted sum of the nodes in the preceding layer, according to the connection weights. As adding hidden layers to a completely linear model does not provide added representational value, a non-linear element is brought in through the use of threshold levels for each hidden layer node. This can be done either through a simple step function, where a node is only activated if the weighted sum of its inputs exceeds a predetermined level, or by transforming the weighted inputs using a suitable continuous function such as the sigmoid function: y ¼ /ðxÞ

1 1 þ ex

This function maps input values to a range of zero to one, with the slope of the function being steep for small input values and shallower for larger inputs. Adding hidden layers gives a network substantially more representational power – Kurkova (1992) and Ito (1994), among others, argue that networks with two hidden layers can describe any continuous function defined on an n-dimensional cube. Thus one of the major advantages of a neural network architecture is its ability to approximate to a very wide range of functional forms (White, 1989; Kuan and White, 1992), and because the network learns from the examples presented to it, we do not have to specify in advance what relationship we expect between independent and dependent variables. From a marketing viewpoint, a neural network with one hidden layer can also be of use in throwing light on latent or unobservable variables, for instance underlying behavioural traits or attitudes which cannot be directly measured. The hidden layer nodes can be labelled by reference to the weights of the connections from the input nodes which feed into them. The underlying philosophy is related to that of the LISREL model (Long, 1983a,b). The Delta Rule learning algorithm, discussed earlier, cannot be applied to a network with hidden layers; obviously it is impossible to compute prediction errors for hidden layer nodes, as they do not have observed values. For a network with one or more hidden layers, the most common learning algorithm is the backpropagation method,


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which can be viewed as an extension of the Delta Rule. Here the observed errors calculated for the output layer nodes are divided pro rata between the hidden layer nodes, giving imputed errors for them, and connection weights are adjusted at each iteration in the way that will give the best improvement in the fit of the model, i.e. they are adjusted proportionally to the first derivative of the function to be optimised (the error function). Rumelhart et al. (1986) offer a proof that this method will find an optimal fit. The network is allowed to run until this optimum, as measured by the Root Mean Square error, is achieved.

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Professor Paul Phillips is currently Charles Forte Chair of Hotel Management and Director of the Centre for Hospitality Industry Performance Research at the School of Management, University of Surrey. Prior to his academic career Paul was a senior management consultant with Price Waterhouse and has held senior line management positions in industry. His current research activities include the performance measurement and performance improvement of hotel organisations, and he has just completed a major textbook entitled ‘‘E-business Strategy: Text and Cases’’ which has been published by McGraw-Hill. Fiona Davies is a Lecturer in Marketing and Strategy at Cardiff Business School, where one of her reseach interests is the use of artificial intelligence techniques in marketing. She has worked for several years on developing and testing prototype expert systems in the marketing and strategy areas, and evaluating the potential of different types of neural network in the analysis of consumer and firm behaviour, e.g. market segmentation, modelling customer satisfaction, and predicting company performance. She has published widely in marketing and management journals. Professor Luiz Moutinho holds the Foundation Chair of Marketing and is the Director of the Doctoral Programme at the Department of Management Studies, University of Glasgow, in Scotland. He holds a PhD in Marketing from the University of Sheffield in England. Current research interests include computer modelling in marketing management, consumer behaviour and the marketing of services. Professor Moutinho has published extensively in academic journals in the UK and the USA.