No abstract

PurposeThe purpose of this article is to investigate the factors that explain the reasons why customers may be willing to use chatbots in Zimbabwe as an e-banking customer service gateway, an area that remains under researched.Design/methodology/approachThe research study applied a cross-sectional survey of 430 customers from five selected commercial banks conducted in Harare, the capital city of Zimbabwe. Hypotheses were tested using structural equation modelling.FindingsThe research study showed that a counterintuitive intention to use chatbots is directly affected by chatbots' expected performance, the habit of using them and other factors.Research limitations/implicationsTo better appreciate the current research concept, there is a need to replicate the same study in other contexts to enhance generalisability.Practical implicationsChatbots are a trending new technology and are starting to be increasingly adopted by banks and they have to consider that customers need to get used to them.Originality/valueThis study contributes to bridging the knowledge gap as it investigates the factors that explain why bank customers may be willing to use chatbots in five selected commercial Zimbabwean banks. This is a pioneering study in the context of a developing economy such as Zimbabwe.

Modelling and forecasting of tourist arrivals at one of the Seven Natural Wonders of the World, the Victoria Falls Rainforest, is critical to the tourism industry and economy of Zimbabwe. The aim of this paper is to provide quantitative techniques that will help with accurate tourist arrivals forecasting, shedding light on seasonality and other patterns of tourist arrivals. A time series plot of the monthly tourist arrivals statistics from January 2006 to December 2017 availed by the Zimbabwe Tourism Authority and Zimbabwe Parks and Wildlife Management Authority shows an upward trend in tourist arrivals with large fluctuations. To tame the variance which is increasing with time, a logarithm transformation is done on the data. A SARIMA (2, 1, 0)(2, 0, 0)12 model fits well to the data and outperformed other SARIMA models and the naïve, seasonal naïve and Holt-Winters exponential smoothing models. A two-year future out-of-sample forecast is done using this model and gives reasonable forecasts that indicate a general rise in tourist arrivals. Investors, tourism managers and the government can make use of such results in order to find effective and efficient solutions to the investment, foreign currency, accommodation, transport and infrastructure development problems and other tourist-related challenges faced by Zimbabweans.

The objectives of the paper is to: (1) adopt the hierarchical forecasting methods in modelling and forecasting international tourist arrivals in Zimbabwe; and (2) coming up with Zimbabwe international tourist arrivals Prediction Intervals (PIs) in Quantile Regression Averaging (QRA) to hierarchical tourism forecasts. Zimbabwe’s monthly international tourist arrivals data from January 2002 to December 2018 was used. The dataset used was before the COVID-19 period and were disaggregated according to the purpose of the visit (POV). Three hierarchical forecasting approaches, namely top-down, bottom-up and optimal combination approaches were applied to the data. The results showed the superiority of the bottom-up approach over both the top-down and optimal combination approaches. Forecasts indicate a general increase in aggregate series. The combined methods provide a new insight into modelling tourist arrivals. The approach is useful to the government, tourism stakeholders, and investors among others, for decision-making, resource mobilisation and allocation. The Zimbabwe Tourism Authority (ZTA) could adopt the forecasting techniques to produce informative and precise tourism forecasts. The data set used is before the COVID-19 pandemic and the models indicate what could happen outside the pandemic. During the pandemic the country was under lockdown with no tourist arrivals to report on. The models are useful for planning purposes beyond the COVID-19 pandemic.

No abstract