Tag Archives: Regression Model

Diagnostics of Customer Satisfaction in the Hospitality Industry: Evidence from Nigeria (Published)

Nigeria in the last few decades has experienced a tremendous growth in the number of public and private hotels. The biggest challenge in today’s competitive business environment is how to retain customers and ensure customer loyalty. Also, Guest relationships in the hotel industry are strategic assets of the organization and customer satisfaction becomes the starting points to defining business objectives. The study therefore analyzed the indices of customer satisfaction in the hospitality industry in Nigeria. The research analyzed the significance of key factors that determine customer satisfaction. A structured questionnaire on staff performance, cost, hotel facilities, environment and porn accessibility was developed and used to collect information from the study sample. The structured questionnaire was administered to 400 respondents purposively. Descriptive statistics and regression analysis were used to analyze the data. The result showed that cost, hotel environment, hotel facilities and income respectively were seen to have a strong impact on customer satisfaction at 5% level of significance, while staff performance seems significant at 10%. There was a relationship between service quality and customer satisfaction. The study makes a significant contribution to the service quality management literature because few empirical studies are available dealing with this aspect of the hotel management in Nigeria. It would also help service providers in the hotel industry to understand aspects of service variables that need urgent improvement.

Keywords: Customer Satisfaction, Hospitality, Regression Model, Service Quality, hotel management.

Habitat Preferences and Effect of Environmental Factors on the Seasonal Activity of Lithobius Nigripalpis L. Koch, 1867 (Chilopoda: Lithobiomorpha: Lithobiidae) (Published)

The activity of animals is defined mostly by internal genetic mechanisms, but physical factors, such as temperature, soil Ph, light duration, and humidity, play a role in the regulation of this biological process. For centipedes, for example, humidity is one of the key environmental factors that determines their distribution and activity. However, abiotic and biotic factors such as soil pH, temperature, vegetation type, and human disturbance remain relatively understudied and little is known on their importance for the centipede activeness. Here we present a study on the habitat preferences and seasonal activity of Lithobius nigripalpis L. Koch, 1867, a species that is widely distributed in the Balkan Peninsula, adjacent parts of Romania, and Anatolia. The study was carried out from May 2007 to May 2009 in the city of Shumen, NE Bulgaria, and its surroundings. Pitfall trapping had been used to determine the seasonal activity and habitat preferences of the species in a range of ecosystems subjected to different degrees of human pressure. Using the software packages SPSS 9.0 and Stat Plus 3.5.3.a number of statistical analyses were employed to test which environmental factors are relevant to the activity and distribution of the species. Our study revealed that in the studied region L. nigripalpis is euryoecious species, which occurs in all habitat types. However, it demonstrates clear preference for undisturbed open habitats, in particular xerothermic shrubby grasslands of the phytocoenose Festuco-Brometea. The current levels of urbanization of the city do not seem to have any significant effect on the distribution and activity of the species concerned. L. nigripalpis exhibits highest activity during summer season (June to September), with peaks in July and August. The environmental factors that have highest significance for its seasonal activity are air and soil temperature, and soil humidity.

Keywords: Analysis Of Variance, Correlation, Habitat Preferences, Lithobius Nigripalpis, Regression Model

CO INTEGRATION: APPLICATION TO THE ROLE OF INFRASTRUCTURES ON ECONOMIC DEVELOPMENT IN NIGERIA (Published)

The study appraised the role of infrastructure on economic development in Nigeria measured by the gross domestic product while the infrastructure is measure with the capital expenditure on Transportation & communication (TRC), Education (EDU) and Health (HLT) respectively for a period of 32 years (1981-2013). Using least square (OLS), we find out that, the measure of coefficient of determination shows that about 95.11% of variation in GDP can be explained by infrastructure. The regression model explain that a unit increase in Transport &Communication(TRC) and Education(EDU) will increase GDP by 237% and 174% respectively, while the Health(HLT) will reduces the GDP by 31%. The residual of the regression model is stationary, when subjected to the unit root test and the Johansen co integration test show that two of the equation is co integrated. From this, it can be affirmed that the regression model are not spurious. The co integrating equation also suggesting that the GDP adjust to change in capital expenditure on infrastructures in the same time period and shows that short-run change in TRC and EDU have negative impact on short-run change in GDP but only HLT has positive impact on GDP in the short run.

Keywords: Co-integration, Economic Development, Infrastructure, Regression Model

INCORPORATING DUMMY VARIABLES IN REGRESSION MODEL TO DETERMINE THE AVERAGE INTERNALLY GENERATED REVENUE AND WAGE BILLS OF THE SIX GEOPOLITICAL ZONES IN NIGERIA (Published)

This paper compare the year 2012 Internally Generated Revenue (IGR) and Wage bills of the six (6) Geopolitical zones in Nigeria. The Internally Generated Revenue and wage bills from the thirty six (36) states were categorized to six (6) Geopolitical zones (Southwest, Southsouth, Southwest, north central, northeast and northwest) and proxy with dummy variables. The average Internally Generated Revenue and Wage bills of each Geopolitical zone were derived from the expectation of the regression model and the estimated coefficient of its slope indicate which of the averages is statistically significant different. It also appraised which of the six geopolitical

Keywords: Dummy Variables, Geopolitical Zones in Nigeria, Regression Model, Wage Bills