Search results

This study aims to explore the impact of store atmospherics on urban bottom-of-the-pyramid consumers’ behavioural intentions to purchase apparel in an emerging African market context. This study also considers purchase antecedents to attitude, perceived behavioural control and social norms as determinants of urban bottom-of-the-pyramid consumers’ apparel behavioural intentions.

Using non-probability sampling, specifically purposive and interlocking sampling, data collection was secured from 881 economically active Namibian urban bottom-of-the-pyramid respondents through interviewer-administered questionnaires. Covariance-based structural equation modelling assessed the significant relationships among all constructs in the conceptual model.

This study found that for favourable apparel behavioural intentions of urban bottom-of-the-pyramid consumers to occur, apparel retailers should emphasise trust, perceived awareness and self-identity through apparel assortment and groupings, easy-to-read visible signage, together with competent, friendly and respectful sales personnel in their store atmospherics.

The findings of this study may guide apparel retailers in other emerging African markets to develop regional integration, market-based solutions and inclusive economic growth focusing on “non-essential” products, such as apparel, among urban bottom-of-the-pyramid consumers.

This study expands the intellectual boundaries of urban bottom-of-the-pyramid consumers’ behavioural intentions towards “non-essential” products. The theoretical framework supports the integration of both the stimulus-organism-response model and the theory of planned behaviour into one single model for empirical investigation. Additionally, adopting a novel theoretical framework helped identify the impact of store atmospherics from a bottom-of-the-pyramid perspective in an emerging African market context, such as Namibia.

This study aims to explore the factors influencing the bottom of the pyramid (BOP) consumers’ adoption and usage intention towards mobile payment (m-payment) to achieve financial inclusion and sustainable development goals.

A qualitative research design is used to explore the enablers and inhibitors that influence BOP consumers’ m-payment adoption and usage intention. To collect the qualitative responses, semi-structured in-depth interviews with BOP respondents were conducted. The thematic analysis using the text mining technique will be used to analyse qualitative data for exploring the predominant factors affecting m-payment adoption intention and usage.

The results suggested awareness, social influences and self-efficacy as crucial enablers and privacy and security risks and vulnerability concerns as crucial inhibitors towards m-payment adoption and usage.

As a novel contribution to the BOP, financial inclusion, sustainable development goals and m-payment literature, this study unfolds several unknown perceived benefits and perceived sacrifices that influence the BOP consumers’ m-payment adoption intention and usage. The study’s findings help the government and banks formulate and implement strategies to achieve financial inclusion among BOP consumers.

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.