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This paper aims to examine the interconnection between marketing and entrepreneurship among small and medium scale enterprises in emerging markets.

Quantitative data were obtained from 113 micro, small and medium scale enterprises (SME) into services, manufacturing and agriculture selected conveniently within the Tema metropolis, a harbour city in Ghana; however, purposive sampling was used to choose owners and managers as respondents pre-occupied with marketing and entrepreneurial roles. These voluntary informants have operated between 4 and 9 years.

The study reveals a significant relationship between five dimensions of the study including market orientation and entrepreneurial success; customer orientation and entrepreneurial success; competitor orientation and entrepreneurial success; intelligence generation; and entrepreneurial success, including information dissemination and entrepreneurial success.

Blending marketing with entrepreneurial initiatives has the propensity to accelerate success for wealth and job creation for national development especially in emerging markets where poverty and under development abounds. Adoption of basic marketing principles enables local entrepreneurs to become vehicles for social re-engineering and for rapid socio-economic growth, which ultimately affects lives at the local level. The study was limited to opinion of SME managers and owners of a harbour city.

Application of basic marketing principles influences entrepreneurial success in emerging markets (EMs) highlighting opinions of managers and owners of SMEs strategy warranting attention of stakeholders. Thus, the study validates theoretical model of how prudent marketing and entrepreneurial attitude contributes entrepreneurial success. It also provides a new perspective on marketing principles and success in emerging markets.

Consciously incorporating basic marketing principles into operations of MSMEs will impact performances; hence, social lives of entrepreneurs will be affected positively.

This study being among the few in sub-Saharan Africa highlights how application of marketing principles to entrepreneurial operations is a vital role in growing local MSMEs unto the world stage. Therefore, blending basic marketing principles with entrepreneurial initiatives will accelerate wealth and job creation and national development to achieve the world's sustainable development goals aimed at reducing poverty.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

While it is argued that entrepreneurship provides considerable freedom, it is also underlined that it might have the potential for exclusion and oppression. The study contributes to this debate and aims to investigate how entrepreneurs with disabilities (EWD) ascribe meaning to freedom in a contested terrain informed by entrepreneurial autonomy as well as constraints due to impairments and an ableist social environment.

The study uses a qualitative approach and builds upon the critical concepts of negative, positive and social freedom as a theoretical lens for the in-depth analysis of the twenty-nine semi-structured interviews with EWD in Hungary.

Findings indicate that EWD experiences freedom in ambivalent ways. Engaging in the discourse of entrepreneurship offers a subversive discursive toolkit to debunk the constraints established by ableism, enabling both negative and positive freedom. However, individualism being at the heart of entrepreneurship results in othering and undermines social freedom. Thus, while entrepreneurship offers greater individual freedom in both a negative and a positive sense for people with disabilities (PWD), it nevertheless fails to promote collective social change.

Contributing to the critical disability literature, findings contrast the view that having an impairment only reduces a person's abilities and highlight that it also affects the very nature of liberty. Contributing to critical studies on entrepreneurship, the case of EWD provides empirical evidence for understanding the simultaneous emancipatory and oppressive character of entrepreneurship through the interplay of the subjective experience of freedom related to disability and entrepreneurship.

This study compares the performance of female majority-owned new ventures (FNV) vs. male majority-owned new ventures (MNV). It analyzes the differences in levels of variables such as education, the same industry work experience of owners, and other venture level attributes between FNVs and MNVs. More importantly, this study employs decomposition techniques to determine the individual contribution from the intergender difference of each attribute on the performance of the new venture. For example, the study finds that, on average, the owners of an MNV possessed 3.4 years more of the same industry work experience than their FNV counterparts. This difference in work experience accounted for 47% of the “explained” gap [1] in Net Profits between the FNVs and MNVs.

This paper utilizes the Kauffman Firm Survey, a longitudinal dataset of 4,928 new ventures started in the USA in 2004. It employs Blinder-Oaxaca and Fairlie decomposition techniques in conjunction with OLS and Logit regressions. Both methods provide point estimates of contributions to the performance gap due to the heterogeneity in each attribute across the groups (FNV and MNV). This approach has a significant advantage over OLS or mediation analysis, which can only provide a directional analysis of the contributions of differences in attributes to performance.

The paper finds no performance gap between MNVs and FNVs. It further investigates whether the heterogeneous characteristics of MNVs vs FNVs are related to different effects on survival and performance. It finds that characteristics such as owners’ work experience in the same industry, average hours worked by owners in the new venture, the technology level of the venture, and its incorporation status are related with a differential impact on new venture survival and performance.

All firms in the dataset belonged to a single cohort (2004) of new ventures started in the US. Future studies are encouraged to develop a dataset from multiple geographies and founding over several years so that the results may be more generalizable.

The paper provides crucial practical guidance to policymakers, investors, and entrepreneurs. In general, policies that enhance the work experience of women entrepreneurs and provide access to infrastructure such as daycares, which may allow them to work more hours, would probably improve the performance of FNVs.

The paper furthers the literature on women entrepreneurship by analyzing point estimates of differential contribution of disparate variables to performance. From a methodological perspective, the study reconciles the results between regression and decomposition analyses.