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This study aims to draw on cue utilization and irradiation theories to: determine the extent to which country-of-origin image and its sub-dimensions exert an aggregate and relative influence on consumer brand evaluations; and identify the contextual and methodological factors that account for between-study variance in the focal relationship.

A random-effects model was used to examine 166 empirical articles encompassing 499,563 observations, and 282 effect sizes from 1984 to 2020 using Comprehensive Meta-Analysis software.

Results show that country-of-origin image has a positive, moderate effect on consumer brand evaluations. Moreover, findings reveal that each dimension of country-of-origin image – general country image, general product country image, specific product country image and partitioned country image – significantly influences consumer brand evaluation, but the effect of general product country image is the largest. What’s more, the aggregate impacts of country-of-origin image on consumer brand evaluation – brand commitment, brand-specific associations and general brand impressions – show that the effect on brand commitment is the largest. Finally, findings show that contextual factors (brand source, product sector, culture [individualism vs collectivism], brand origin continents and respondents’ continent) and methodological factors (cues, sampling unit, publication year and sample size) significantly account for between-study variance.

This study provides the first meta-analytic review of the relationship between country-of-origin image and consumer brand evaluation to help clarify mixed findings and balance out the literature, which has only seen quantitative reviews on product evaluation and purchase decisions.

The purpose of this study is to clarify the findings and criticisms in the extant literature concerning the theory of consumption values (TCV) by conducting a meta-analysis to (1) examine the extent to which consumption values influence consumer behavior and (2) to explore contextual and methodological factors that may account for between-study variance in the focal relationship.

The study employs a random-effects model and psychometric meta-analysis approach to examine 82 studies with 297 effect sizes in 34 countries between 1991 and 2022, inclusive.

Results reveal that consumption values have a positive significant and moderate effect on consumer behavior. Moreover, emotional value is the most influential predictor of consumer behavior, while social value is the weakest. Furthermore, the study's findings show that some contextual and methodological factors moderate the relationship between consumption values and consumer behavior.

The findings highlight that managers can work on consumption values to prompt positive consumer responses like attitude, intention, satisfaction and overall value perception. However, managers must consider that the relevance of the consumption values depends significantly on the outcome variable and the context, which calls for a tailored-made marketing strategy to appeal to consumers' diverse needs and wants.

Besides providing empirical evidence of the broad validity of the TCV, this study is the first meta-analytic review of the TCV, which integrates several insights to provide valuable research directions for future researchers and insightful implications for practitioners.

Informed by the resource-based view of the firm, dynamic capabilities theory and contingency theory, this study examines the impact of Industry 4.0 (IR4.0) technologies adoption on firm performance (FP) while accounting for the mediating role of innovation ambidexterity (IA) and moderating roles of contextual and methodological factors that drive the performance gains of the phenomenon.

A random-effect model in comprehensive meta-analysis (CMA) is used to synthesize 113 studies in 115 independent samples with 192,188 observations.

This analysis demonstrates that IR4.0 digital technologies are directly related to financial and non-financial performance, disclosing that the performance effect on non-financial is the largest. Moreover, there is a complementary partial mediation role of the impacts of IR4.0 on FP by IA. Furthermore, this focal relationship is moderated by boundary-spanning conditions: contextual factors – firm size, business type, economic development, industry sector and methodological factors – proxy of FP, sample size and study type.

The results imply that IR4.0 produces financial and non-financial benefits by enabling firms to develop dynamic capabilities like innovation ambidexterity, which informs managers and practitioners that unless IR4.0 technologies and IA strategies are combined together to generate superior FP, IR4.0, in and of itself, would produce a less positive impact on FP than the combined impact of IR4.0 and IA. Therefore, managers should focus on converting IR4.0 resources to dynamic capabilities like IA by leveraging open innovation strategies or building IR4.0-based coordination mechanisms by creating cross-unit business synergies.

To the best of the authors' knowledge, per the literature review, this is the first meta-analysis structural equation modeling study on the interplay between IR4.0, innovation ambidexterity and firm performance.

Several waste bin sanitation initiatives have been introduced in Ghana to address the surge in indiscriminate solid waste disposal in households. What is not known are the behavior factors that determine the acceptability and use of waste bins. This study aimed to identify the determinants of waste bin acceptability and use in Ghana using the theory of planned behavior (TPB).

Data on waste bin acceptability and usage were collected from 881 households in the Volta and Oti regions of Ghana. The data were analyzed using the partial least squares-structural equation modeling technique in SmartPLS 3 software.

The coefficient of determination (R-squared value) of the original TPB and the extended model explained 39.9 and 44.7% of the variance in waste bin acceptability and use intentions, respectively. The results revealed that attitudes (ß = 0.114, t = 3.322, p < 0.001), subjective norms (ß = 0.306, t = 6.979, p < 0.001) and perceived moral obligation (ß = 0.352, t = 8.062, p < 0.001) significantly predicted household waste bin acceptability and use behavior intentions, but perceived behavioral control (ß = −0.003, t = 0.064, p < 0.949) did not influence behavior intentions significantly.

The study provides valuable insights into the behavioral factors to be prioritized by waste management service providers to improve household waste bin acceptability and usage.

This is one of Ghana's first studies investigating the behavioral determinants of waste bin acceptability and usage.

Having the right intent, aspiration, ability and attitude to become an entrepreneur has become the mantra in the extant literature to be driver of entrepreneurship and small and medium enterprise (SME) growth. Why would zealous and ambitious individuals with all rightful attributes so required of entrepreneurs have to fizzle out few years after venturing into business or SMEs? Perhaps these same individuals may relocate to other jurisdictions and would establish successful firms even beyond their imaginations. Beyond the individual’s entrepreneurial attributes, there are other external countervailing forces which either “enable” or “impede” entrepreneurial drive and SME growth processes. Adopting the theory of planned behavior, this study conceptualizes a systems framework to analyze how SMEs either flourish or fail in developing countries.

The study relies on secondary sources of data. It adopts a critical stage review of secondary data.

The study argues that the interplay of “internal factors” and “external factors” of prospective entrepreneurs provides a useful framework to explain the general SME outlook of an economy. The study postulates that many internally driven prospective SME entrants (with entrepreneurial attitudes, abilities and aspirations) mostly in the developing economies may have their dreams shattered because of obstructive external ecological elements which tend to frustrate new business entrants as well as existing ones.

With the aid of a framework, this study conceptualizes a comprehensive framework to analyze how SMEs either flourish or fail in developing countries.

The proliferation of surveillance-enhancing laws, policies and technologies across African countries deepens the risk of privacy rights breaches, as well as the risks of adverse profiling and social sorting. There is a heightened need for dedicated advocacy and activism to consistently demand accountability and transparency from African states, governments and their allies regarding surveillance. The purpose of this paper is to understand the issue frames that accompany anti-surveillance and privacy advocacy in Ghana and the related implications.

Using a qualitative and interpretivist approach, the author focuses on three different surveillance-oriented incidents/programs in Ghana and analyzes the frames underpinning the related advocacy and narratives of various non-state actors.

Privacy and anti-surveillance advocacy in Ghana tends to be less framed in the context of privacy rights and is more driven by concerns about corruption and value for money. Such pecuniary emphasis is rational per issue salience calculations as it elevates principles of economic probity, transparency and accountability and pursues a high public shock value and resonance.

Economics-centered critiques of surveillance could be counterproductive as they create a low bar for surveillance promoters and sustains a culture of permissible statist intrusions into citizens’ lives once economic virtues are satisfied.

While anti-surveillance and privacy advocacy is budding across African countries, little is known about its nature, frames and modus compared to such advocacy in European and North American settings. To the best of the author’s knowledge, this is likely the first paper or one of the first dedicated fully to anti-surveillance and advocacy in Africa.

This study examined the impact of the Input Credit Scheme (ICS) by the Integrated Water Management and Agriculture Development (IWAD) on the productivity and food security of smallholder rice farmers in Ghana.

Cross-sectional data from 250 rice farming households in the Mamprugu Moagduri district of the North East Region obtained from a multi-stage sampling technique were used for the study. Inverse Probability Weighted Regression Adjustment (IPWRA), Propensity Score Matching (PSM) and Kendall's coefficient of concordance were the methods of analysis employed.

Empirical results show that education, rice farming experience, dependency ratio, FBO membership, farm size and farm age were the significant factors influencing participation in the input credit scheme (ICS). Also, participants had an average rice productivity of 1,476.83 kg/ha, whereas non-participants had 1,131.81 kg/ha implying that participants increased their productivity by about 30%. In addition, the study revealed that participant households increased their household dietary diversity (HDDS) by 0.45 points amounting to about 8% diversity in their diets. High-interest rates associated with credit received, the short periods of credit repayment and the high cost of inputs provided under the scheme were the most challenging constraints associated with partaking in the ICS.

The available literature on agricultural interventions have predominantly emphasized input credit as a key factor for improving cropt productivity and food security of smallholders. This study provides compelling evidence that participation in ICSs can result in substantial benefits for agricultural development, as evidenced by increased productivity leading to improved food security. The significance of these findings is highlighted by the fact that, through participation in input credit schemes, smallholder rice farmers in many developing countries see substantial improvement in their capacity to access productive resources, thereby improving their productivity, while simultaneously reducing food insecurity.

Leveraging on the improved productivity of participants in the ICS, this study advocates that such input credit schemes should scale up to more food-insecure farming communities in Ghana.

The study uses a doubly robust econometric approach to evaluate the impact of ICS on smallholder rice farmers' productivity and food security in Ghana, making it the first of its kind. The findings offer a solid basis for future research and provide guidance for policymakers looking to boost agricultural development in Ghana.

This study aims to analyse the resilience strategy utilized by small and medium-sized enterprises (SMEs), enabling these businesses to effectively adapt their operations in response to varying conditions by providing them with essential resources. SMEs operate in marketplaces that are both dynamic and frequently tumultuous. These markets provide SMEs with a variety of obstacles, including economic ups and downs, advances in technology, evolving customer tastes and new regulatory requirements. SMEs need to create a strategic strategy to survive and grow in such situations. This strategy ought to help strengthen their resiliency and make it possible for them to make the most of emerging opportunities while simultaneously lowering the dangers.

The questionnaires adopted and adapted from previous research served as the basis for gathering the data. The manufacturing industry was polled through the use of questionnaires. To test the hypothesis, the data were analysed using Smart PLS. Through the use of closed-ended questions directed to the proprietors, managers or senior executives of SMEs, data were collected from each and every institution in the sample. Following the examination of the data by means of descriptive analysis and the presentation of several scenarios using information relating to SMEs, the findings were presented.

The ambidextrous strategies that are used by SMEs have a propensity to offer a constructive contribution to SMEs. In this study, it was discovered that ambidexterity, which is defined as the capacity to both seek and capitalise on possibilities, has a significant bearing on the organisational effectiveness of SMEs. The results showed that ambidextrous strategies have a propensity to work as mediators in interactions involving proactive resilience tactics and performance.

The research expands our understanding of how SMEs in the manufacturing sector may improve their performance by concentrating on growing their ambidextrous strategies.

This study provides a plausible explanation of two crucial management mechanisms for enhancing the sustainability of organisational effectiveness. The relationships between ambidextrous capabilities and firm effectiveness are malleable, and this study suggests that nurturing formal and informal relationships may be the key to SMEs' long-term sustainable performance. Improving the knowledge and performance of supply chain systems for SMEs in the manufacturing sector and boosting their competitiveness in domestic and international markets are the practical contributions of this study.

Our comprehension of monitoring, cooperation and innovation within social management was deepened as a result of these facts. In addition, the study conducted in the sector uncovered four essential connections that outline how managers should actively work towards lowering social risks, developing new possibilities and increasing business performance. These capacities and links, when taken as a whole, provide the foundation upon which an integrated framework and five research propositions are built.

This research offers a convincing explanation of fundamental management processes for enhancing the sustainability of organisational effectiveness. This research implies that developing formal and informal interactions may be the key to the sustainable performance of SMEs over the long run. The relationships between ambidextrous capabilities, methods and organisational effectiveness are flexible, and this study also suggests that these relationships may be shaped. The practical contributions made by this research include boosting the understanding and performance of supply chain systems for SMEs as well as the competitive power of these businesses in both local and international markets.

In light of the recent changes in the internal audit (IA) landscape, the role of auditors has undergone a significant transformation. This paper aims to investigate the effects of applying Lean Six Sigma (LSS) techniques on the effectiveness and efficiency of IA.

The study used a quantitative approach, surveying Iranian internal auditors with a sample size of 384 participants. Data analysis involved confirmatory factor analysis and structural equation modeling.

The analyses demonstrate a significant association between LSS application and IA effectiveness and efficiency. In addition, an exploratory analysis indicates that the application of LSS techniques by less experienced internal auditors had a reverse effect on IA function quality as a component of IA competency. However, IA motivation factors, including education and position, did not mediate the impact of LSS on IA effectiveness and efficiency.

This study was conducted with Iranian internal auditors, which may limit the generalizability of the findings to other countries. However, the primary academic implication of this research lies in its novel perspective on emphasizing the concept of continuous improvement in IA through the use of LSS techniques. By focusing on the need for internal auditors to add value to the business in new ways, this research contributes to the literature on IA quality.

This study has significant implications for the effective management of IA departments. By promoting the application of LSS techniques in IA, lean auditing is enhanced, and IA can create value by improving the quality of its functions. Moreover, IA regulators can benefit from this study as it emphasizes providing guidance and training on LSS techniques to enhance IA skills.

This research is pioneering in applying LSS methodology to enhance the effectiveness and efficiency of internal auditing. It also considers the integration of lean thinking into current audit practices, making it unique and valuable in internal auditing research.

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.