Data Presentation and Analysis - Investigating Internet Marketing Strategies among Hotels in Ghana

4.9 Data Presentation and Analysis
Copper and Schindler (2003) are of the view that, virtually all research involves some numerical data that usually could be quantified to help answer research questions and to meet objectives. Data collected need to be analyzed and interpreted. Depending on the nature of study there are numerous statistical techniques for analyzing data.
For analyzing data gathered from questionnaires, several techniques are available in statistical tools such as SPSS. To answer our research questions simple frequencies and percentages are applied to know the number and proportion of hotels regarding the transformation of aspects in each of the internet marketing mix dimensions. We chose to analyze our data using general frequency because it gives the researcher the opportunity to present detailed information on nominal (category) data and describing the results which is consistent with the focus of this study to present in detail the composition of the internet marketing mix. Also the frequency distribution was presented in pie and bar graphs. Visually illustrating the data this way can help a reader make sense of the findings. These analyses were executed using the Multiple Response facility of SPSS. SPSS (Statistical Package for the Social Sciences) has been in development for more than thirty years. Originally developed as a programming language for conducting statistical analysis, it has grown into a complex and powerful application which now uses both a graphical and a syntactical interface and provides dozens of functions for managing, analyzing, and presenting data. Its provide researchers with a wide range of statistical capabilities from simple percentages to complex analyses of variance, multiple regressions, and general linear models. Also data ranging from simple integers or binary variables to multiple response or logarithmic variables can also be used. In additions SPSS provides extensive data management functions, along with a complex and powerful programming language. Besides the all the capabilities of SPSS, the decision to use it to analyse our data was also due to our familiarity in its use.
The Multiple Response command allows us to analyze a number of separate variables at the same time, and is best used in situations where the responses to a number of separate variables that have a similar coding scheme all ‘point to’ a single underlying variable. In this study we consider each of the items in each of the five internet marketing dimensions (Product, Place, Promotion and Customer Relation) as all pointing to transformation of the dimensions. Each of the items in the questionnaire captures just one aspect of the complex variables (marketing mix dimensions). It is therefore appropriate to summarize the responses to these items at once, and to be able to use the pattern of responses across these items in further analysis with other variables, which is exactly what the Multiple Response command allows us to do.
To use the Multiple Response command a Multiple Response Set for each of the dimensions were set up for each of the internet marketing mix dimensions. This procedure instructs SPSS to group together the responses across each variable. The coding scheme for the items that make up the Multiple Response Set are “1=Yes and 2=No. the coding scheme is noted because we need to tell SPSS which value is of interest to us. Here we are interested in all the Yes responses to each item.
As indicated in chapter three, to measure the sophistication of the Internet marketing strategies, each of the five dimensions of the model were further analyzed in several aspects/features that indicate the degree to which hotels have adopted sophisticated strategies. Items in the questionnaire match with the aspects/features within the five dimensions in table 3.1 of chapter three. The degree to which hotels have adopted sophisticated internet marketing strategies depends on the responses to the questionnaire items by each of the surveyed hotels. Consistent with Sigala (2003) and Angehrn’s (1997) analysis of the Internet marketing mix model, the surveyed instrument of each dimension are characterized as low or high sophistication depending on whether the interactive and connectivity capabilities of the Internet are used.
To measure the degree of transformation of each dimension, the sum of the aspects that the respondent answered “Yes” and the total number of aspects within the dimension from the multiple response output for each dimension are used. The transformation degree of each dimension was calculated by the ratio of the sum of the aspects used to the total number of the aspects within the dimension. Each feature carried the same weight of one (1) meaning if all the hotels uses all the aspects within a dimension then the transformation degree for that dimension would be one (1) or 100%. A transformation degree less than 0.5 or 50% indicates a low transformation/sophistication meaning hotels make limited use of the capabilities offered by the internet whiles a transformation degree above 0.5 or 50% indicates high transformation meaning hotels are taking advantage of the unique capabilities offered by the internet.
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