Search results

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

This study focuses on the factors that determine the readiness of hotels to implement customer relationship management (CRM) in hotels within the context of Ghana. The sample consisted of 292 employees (restaurant managers, customer service officers, customer relations' officers, and marketing managers) from 3- to 5-star hotels. The study adopted a quantitative deductive approach to collected data using cross-sectional survey, which was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that management change initiatives and culture have significant impact on organizational readiness to implement CRM in hotels, specifically Ghana. Also, the organizational culture partly mediates management change initiatives and organizational readiness to implement CRM activities. On the other hand, use of technology proved not to mediate management change initiatives and organizational readiness as the relationship proved not to be significant. Also, culture and use of technology have not mediated management change initiatives and organizational readiness as the indirect path proved not to be significant. The outcomes have useful implications for CRM adoption by hotel managers.

This study examines organizational innovation in small- and medium-sized enterprises (SMEs) and develops an extensive framework of how innovation occurs, its end results in terms of positive, negative outcomes, and its impacts on business financial performance; using grounded methodology; interviews with entrepreneurs and executive experts from across industries. The study aims to fill gaps in the literature. Despite extensive research conducted on innovation, most focus on factors behind innovation and a company's innovativeness. The framework is useful to SMEs considering company-wide innovation. Transparent inputs and outputs enable companies to understand innovation processes, and its outcomes better as well as help monitor and implement individual innovation activities. The framework has a wide application, particularly, in an industry where innovation is hard to capture and understand. Using the model, we can determine innovation drivers, practices, and barriers as well as innovation inputs/outputs in different industries, thus promoting better management of innovation across a wide range of applications. Governments also require a better understanding of innovation, productivity, and operational efficiency to plan their policies in the promotion of innovation.