Effective Interfirm Collaboration - Essay Example

? Marketing Introduction There are different factors that determine the failure of success of a company. In this study, the factors that affect company’s performance will be examined. Two companies will be selected for the study, both from the retail sector, and then a comparative analysis on the factors that affects their performance undertaken. The study will involve survey that will be conducted through a questionnaire, which will be filled by 30 managers from different stores of the retail companies. The managers will be required to fill a number of questions, which will provide important information on both dependent variables and the independent variables. To begin with, the researcher will conduct an extensive analysis on secondary sources such as internet, books and academic journals with the aim of finding the possible factors that are likely to determine performance of a company, and then use the insight to design the questionnaire. Based on the result of the questionnaire, the factors that seem to carry the heaviest weight will be selected and used as the dependent and independent variables for statistical analysis. In this regards, change in sales since 2000 and change in demand have been selected as the dependent variables. The predictor variables will include innovativeness, specialization, lasting customers relations and high-quality marketing strategies. Nowadays, many companies have adopted very advanced technology, which keeps changing day in day out and, therefore, it becomes very important to uphold innovativeness all through. Any company that is not innovative is liable to be thrown out of market by those, which are innovative. This is the reason innovativeness seems to cause considerable impact on performance of many firms (Carter et al., 1994). Furthermore Dyer (1997) affirms that a company can increase its productivity through specialization, which leads to adoption of a particular niche based on its core business. Taylor (1997) has cited customer relations as the most important factor helping firms to become successful. This is because customer is considered the most important party in a business, and if they are treated well they keep coming back. Marketing strategy is meant to publicize the products of a company, and if a company is very committed to it and designs high-quality marketing plans, then the customers is attracted to purchase products from the company hence boosting sales (Wynarczyk et al., 1993). Conceptual Model (CM) Company 1 Company 2 3.0 Regression Analysis 3.1 Linear Additive Model for entire sector PF1 = [X1, X2, X3, X4] Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .940a .884 .866 .766 .884 47.711 4 25 .000 a. Predictors: (Constant), X4, X1, X2, X3 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 112.114 4 28.028 47.711 .000b Residual 14.686 25 .587 Total 126.800 29 a. Dependent Variable: PF1 b. Predictors: (Constant), X4, X1, X2, X3 Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .516 .871 .593 .558 X1 -.128 .094 -.118 -1.362 .185 X2 .625 .133 .468 4.705 .000 X3 .321 .111 .493 2.900 .008 X4 -.012 .090 -.021 -.131 .897 When PF1 (Change in sales since 2000) is compared with the four indecent variables, the above regression and correlation results are found. The adjusted R squared is very high at 86.6%, which indicates that the model is not subject to a lot of errors. Only 13.4% of the prediction is attributable to error and hence the model is quite reliable. The p-value, which is indicated in the table as Sig. is less than 0.005 implying that there is enough evidence that the dependent variable (PF1), has some statistical relationships with at least one of the four predictors. Amongst the four predictor, X2 has the strongest prediction power as indicated by the Sig. figure, which is less than 0.001. PF2 = [X1, X2, X3, X4] Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .655a .428 .337 1.242 .428 4.685 4 25 .006 a. Predictors: (Constant), X4, X1, X2, X3 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .655a .428 .337 1.242 .428 4.685 4 25 .006 a. Predictors: (Constant), X4, X1, X2, X3 When the predictor variables are run against PF2 (Has demand increased?), the model becomes very weak as shown by the Adjusted Square, which is very low at 33.7%. This means that the biggest portion is attributable to error and hence the model is very unreliable. Furthermore, the p-value is > 0.005 and, therefore, we conclude that there is no enough evidence to show that the dependent variable can be predicted by any of the independent variables. In this regards, this model can be dropped as it adds no value in this study. 3.2 Linear Additive Model for entire company 1 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .938a .880 .861 .481 .880 45.953 4 25 .000 a. Predictors: (Constant), X3, X1, X2, X4 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 42.517 4 10.629 45.953 .000b Residual 5.783 25 .231 Total 48.300 29 a. Dependent Variable: PF1 b. Predictors: (Constant), X3, X1, X2, X4 This model is strongly fit for predicting the dependent variable since the Adjusted R squired is very high at 86.1%. The p-value is very close to zero, implying that there is a strong evidence to show that at least one of the predictor variables can be used to predict the dependent variable. 3.3 Linear Additive Model for entire company 2 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .912a .832 .811 .425 .832 40.818 4 33 .000 a. Predictors: (Constant), X4, X1, X2, X3 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 29.509 4 7.377 40.818 .000b Residual 5.964 33 .181 Total 35.474 37 a. Dependent Variable: PF1 b. Predictors: (Constant), X4, X1, X2, X3 Although this model is not as accurate as that of company one, at 81.1% of the Adjusted R squired is a sizeable figure to make the model fit for the prediction. Furthermore, p-value is very close to zero; therefore, there is significant evidence to prove the association between the variables. 4.0 t-tests Analyses: 4.1 Comparative analysis of means using Company as a grouping variable; H0: ?C1 = ?C2 [No significance difference in performance amongst companies] One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper C1 11.459 29 .000 2.700 2.22 3.18 C2 13.577 29 .000 2.500 2.12 2.88 From the table above p-value (Sig.) is less than 0.05; therefore, we reject the null hypothesis and conclude that there is significant difference in performance between the two companies. 4.2 Comparative analysis of means using Gender as a grouping variable; H0: ?M= ?F [No significance difference based on gender] One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper MALE1 5.385 29 .000 .500 .31 .69 MALE2 7.077 29 .000 .633 .45 .82 From the table above p-value (Sig.) is less than 0.05; therefore, we reject the null hypothesis and conclude that there is significant difference based on gender of managers. 4.3 Comparative analysis of means using Age as a grouping variable. H0: ?A1 = ?A2 [No significance difference based on age groups] One-Sample Test Test Value = 0 t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper AGE1 21.087 29 .000 43.300 39.10 47.50 AGE2 24.840 29 .000 41.867 38.42 45.31 From the table above p-value (Sig.) is less than 0.05; therefore, we reject the null hypothesis and conclude that there is significant difference based on age groups of managers. Conclusion and recommendation The results of this analysis have shows that the performance of companies in the retail sectors is largely influenced by the predictor valuables that have been tested. In particular, specialization and customer relations has been seen to carry more weight in terms of predicting performance of the companies, which means that managers of these companies should give them a priority during their strategic planning. Nevertheless, demand of the company’s products have been found to have no significant relationship with the predictor variables, and hence further studies should be conducted to find out whether there are other means that this variable could affect a company, or whether there are some hidden variables that obstruct the relationships. References Carter, N. and M. Williams & P. Reynolds., 1997. Discontinuance among new firms in retail: the influence of initial resources, strategy, and gender. Journal of Business Venturing 12, pp. 125-145. Dyer, J., 1997. Effective interfirm collaboration: how firms minimize transaction costs and maximize transaction value. Strategic Management Journal 18, pp. 535- 556. Taylor, B., 1997. Secrets of the ’supergrowth’ league, in Mastering enterprise, S. Birley & D. Muzyka (eds.), 241-244. London: Pitman. Wynarczyk, P. et al., 1993. The managerial labour market in small and medium sized enterprises. London: Routledge. APPENDICES QUESTIONNAIRE THE MANAGER Gender 1. Male 2. Female What is your age? _________ THE COMPANY What is the company’s origin? How often have the company’s management principles and practices adjusted over the years? 1. Significantly 2. To some extent 3. No changes at all How has the company’s sales changed since 2000 1. Fallen 2. stayed at the same level 3. grown slightly 4. grown fast Has the company engaged in any take over? 1. Yes 2. No DEMAND AND COMPETITIVENESS How has the demand for the company’s products appeared for the last 10 years? 1. Have decreased considerably 2. Have declined slightly 3. Have not grown 4. Grown considerably How has the firm faired compared with its competitors 1. Better than many competitors 2. Slightly better than most competitors 3. At the same level with the rest of the competitors 4. Somehow weaker than most of the competitors 5. Very weak compared with other competitors INNOVATIVENESS AND TECHNOLOGY Which of the following statements best describe your company’s products? 1. They are unique in the market 2. They are hard to get in the market 3. They are not very different from those offered by our competitors 4. They are the same as those offered by our competitors Which of the following statements best describes your company’s level of technology? 1. Managerial know-how is ahead of that of the competitors 2. Managerial know-how is the same as that of the competitors 3. Managerial know-how is lower than that of the competitors Which of the following statements bests suits your company’s attitude towards research and development? 1. Our company strives to be a leader in research and development in order to enjoy the opportunities that emerging markets presents 2. Our focus is on improvement of the current products only 3. Our company avoids incurring research costs and the risk of investing in it 4. Our firm concentrates on the existing products to cut down costs of research and development. SPECIALIZATION Which of the following statements best describe the nature of your firm’s products? 1. Our company has focused on production of one product line 2. Our company produces a variety of product lines Which of the following statement best describes your customers? 1. The company has one major segment that is focused on 2. The company has several segments that serves customers with distinct characteristics 3. The company cannot clearly define its customer segments since they are fragmented Which of the following is the best description of your company’s competitive power? 1. Low 2. Quite good 3. Good SUCCESS FACTORS Kindly assess the significance of the following factors for the success of the firm (Scale 1-7: 1 = not significant at all, 7 = extremely significant). 1 2 3 4 5 6 7 Quality of inputs and dependable suppliers 1 2 3 4 5 6 7 good quality information of customers and their needs 1 2 3 4 5 6 7 Ability to react flexibly to customers’ unique requirements 1 2 3 4 5 6 7 Low transportation and delivery costs 1 2 3 4 5 6 7 lasting customer relations 1 2 3 4 5 6 7 high-quality marketing skills 1 2 3 4 5 6 7 first-class after-sales service 1 2 3 4 5 6 7 High quality products 1 2 3 4 5 6 7 Good name of the company 1 2 3 4 5 6 7 Fast and dependable delivery 1 2 3 4 5 6 7 uncomplicated and low-cost production method 1 2 3 4 5 6 7 Continuous development 1 2 3 4 5 6 7 Customer feed-back 1 2 3 4 5 6 7 Personnel training 1 2 3 4 5 6 7 Availability of skilled employees 1 2 3 4 5 6 7 Continuity of personnel 1 2 3 4 5 6 7 Quality of management 1 2 3 4 5 6 7 good quality information and control systems 1 2 3 4 5 6 7 Planning 1 2 3 4 5 6 7 Environmental scanning 1 2 3 4 5 6 7 Strong growth in demand 1 2 3 4 5 6 7 expectation of new business opportunities Which of the above factors do you think is the most important for the company’s success?..................................................................................................................... Which of the above factors do you think is the most important for the company’s survival?..................................................................................................................... THANK-YOU… Data COMPANY 1 Store no. PF 1 PF 2 X1 X2 X3 X4 AGE Male Female 1 4 3 1 3 7 7 29 1 0 2 1 2 4 1 1 1 36 0 1 3 4 4 1 3 7 6 62 1 0 4 1 2 4 1 1 2 46 1 0 5 3 4 2 1 5 6 52 0 1 6 4 3 1 3 6 6 61 0 1 7 2 2 3 3 4 5 41 1 0 8 1 2 4 1 1 2 45 1 0 9 4 3 1 3 7 6 25 0 1 10 3 4 2 1 5 4 51 0 1 11 1 1 4 1 2 2 43 1 0 12 3 4 2 3 5 6 56 0 1 13 4 4 4 3 6 7 63 1 0 14 2 2 3 1 5 4 54 0 1 15 1 1 1 1 2 1 42 1 0 16 4 3 4 3 6 7 26 0 1 17 3 1 2 3 6 5 36 0 1 18 1 1 4 1 1 2 45 0 1 19 4 3 1 3 7 6 36 1 0 20 2 2 3 2 1 2 45 1 0 21 4 4 1 3 4 3 34 0 1 22 1 2 4 1 1 2 29 1 0 23 4 2 1 3 7 6 47 0 1 24 3 4 2 1 5 6 59 0 1 25 4 2 1 2 4 4 56 0 1 26 1 1 4 1 2 3 38 1 0 27 4 4 1 3 7 7 36 0 1 28 3 2 2 2 6 5 36 1 0 29 1 2 4 1 2 3 25 1 0 30 4 3 1 3 6 6 45 1 0 PF 1 – Change in sales since 2000 PF 2- Has demand increased? X1- Innovativeness X2- specialization X3- lasting customers relations X4- High-quality marketing skills COMPANY 2 Store no. PF 1 PF 2 X1 X2 X3 X4 AGE Male Female 1 3 4 3 3 5 6 35 1 0 2 2 3 2 2 4 5 29 1 0 3 4 3 1 3 6 7 59 1 0 4 2 2 2 2 5 6 48 1 0 5 3 3 1 3 5 4 57 0 1 6 3 3 1 3 7 6 41 1 0 7 2 2 2 1 4 5 41 1 0 8 1 2 4 1 3 2 32 1 0 9 3 3 2 3 6 6 30 0 1 10 3 2 3 3 5 6 28 0 1 11 2 2 2 2 2 1 45 1 0 12 3 4 3 2 3 3 45 0 1 13 4 4 1 3 5 7 45 1 0 14 1 1 3 1 1 1 46 0 1 15 2 2 2 2 3 2 39 1 0 16 2 2 3 1 4 3 42 1 0 17 4 4 1 5 5 6 37 1 0 18 2 2 3 2 3 3 49 0 1 19 2 3 3 2 3 2 30 0 1 20 4 4 1 5 5 5 50 1 0 21 1 1 1 1 1 2 39 1 0 22 2 3 2 2 2 3 29 1 0 23 4 4 1 3 5 6 48 1 0 24 3 4 2 3 7 6 45 0 1 25 4 2 1 3 5 6 25 1 0 26 1 1 4 2 1 2 36 1 0 27 2 1 2 2 2 1 48 0 1 28 3 3 2 2 4 5 54 0 1 29 1 2 4 1 3 2 52 1 0 30 2 3 5 2 4 56 0 1 PF 1 – Change in sales since 2000 PF 2- Has demand increased? X1- Innovativeness X2- specialization X3- lasting customers relations X4- High-quality marketing skills THE WHOLE RETAIL SECTOR (TWO COMPANIES COMBINED) PF 1 PF 2 X1 X2 X3 X4 AGE Male Female 7 7 4 6 12 13 35 1 0 3 5 6 3 5 6 29 1 0 8 7 2 6 13 13 59 1 0 3 4 6 3 6 5 48 1 0 6 7 3 4 11 12 57 0 1 7 6 2 6 13 13 41 1 0 4 4 5 4 8 10 41 1 0 1 4 8 2 4 4 32 1 0 7 6 3 6 11 12 30 0 1 6 6 5 4 10 10 28 0 1 3 3 6 3 4 3 45 1 0 6 8 6 5 8 9 45 0 1 8 8 5 6 11 14 45 1 0 3 3 6 2 6 5 46 0 1 3 3 3 3 5 3 39 1 0 6 5 7 4 10 10 42 1 0 7 5 3 8 11 11 37 1 0 3 3 5 3 4 5 49 0 1 6 6 7 5 10 8 30 0 1 6 6 2 7 6 7 50 1 0 5 6 2 4 5 5 39 1 0 3 5 6 3 3 5 29 1 0 8 5 2 6 12 12 48 1 0 6 6 3 4 12 12 45 0 1 8 6 2 5 9 10 25 1 0 2 4 8 3 3 5 36 1 0 6 2 3 5 9 9 48 0 1 6 5 3 4 10 10 54 0 1 2 5 6 2 5 2 52 1 0 7 4 5 6 7 2 39 1 0 Read More