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Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research.

The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years.

In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population.

Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research.

Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.

Cohen's d, which indexes the difference in means in standard deviation units, is the most popular effect size measure in the social sciences and economics. Not surprisingly, researchers have developed statistical procedures for estimating sample sizes needed to have a desirable probability of rejecting the null hypothesis given assumed values for Cohen's d, or for estimating sample sizes needed to have a desirable probability of obtaining a confidence interval of a specified width. However, for researchers interested in using the sample Cohen's d to estimate the population value, these are insufficient. Therefore, it would be useful to have a procedure for obtaining sample sizes needed to be confident that the sample. Cohen's d to be obtained is close to the population parameter the researcher wishes to estimate, an expansion of the a priori procedure (APP). The authors derive the necessary mathematics, provide computer simulations and links to free and user-friendly computer programs, and analyze real data sets for illustration of our main results.

In this paper, the authors answered the following two questions: The precision question: How close do I want my sample Cohen's d to be to the population value? The confidence question: What probability do I want to have of being within the specified distance?

To the best of the authors’ knowledge, this is the first paper for estimating Cohen's effect size, using the APP method. It is convenient for researchers and practitioners to use the online computing packages.

The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.

Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.

The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.

In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

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.

After expressing an initial disquiet about the nature of many studies that are published using structural equation modelling (SEM), a rationale for using the technique is provided. Given the advantages provided by the technique, the differences between covariance-based and partial least squares techniques are briefly described. The argument progresses by indicating assumptions behind the techniques and what it is that referees require before being able to properly referee the paper. Some issues are fundamental to survey-based materials and include the requirement to distinguish between importance and discriminatory power, and the over-dependency on cross-sectional analysis when making claims of generalisation. Other issues of scale creation and sample size are touched upon. This paper finishes by suggesting a checklist for referees who are asked to review papers using SEM.

This micro-level study unpacks the recruitment and retention of international professionals to small and medium-sized enterprises (SMEs). The study highlights the influence of the founders' international experience when applying organisational-level (meso) policies and practices. With their insider experience as skilled migrants, we share how the founders in each of the SMEs mobilised career capital into human resource management (HRM) strategies.

Combining literature on SMEs and skilled migrants' careers, we draw upon intelligent career theory to illuminate the recruitment and retention of self-initiated expatriates and skilled migrants in SMEs. With three SME case studies as samples–one micro, one small and one medium-sized organisation in Ireland–we consider the influence of the founders' international experience in the design and application of formal and informal HRM strategies (at the organisational level) that are operationalised to recruit and retain international talent to/in these organisations.

The HRM practices in the three SME cases in this paper, each run by migrant founders, vary from formalised (for our medium-sized organisation), semi-formalised (for our small-sized organisation) to ad hoc and tailor-made (for our micro-sized organisation). These particular SMEs were often more receptive to hiring other migrants. The important role of the three SME case studies' skilled migrant founders and their own international career experiences was apparent in the particular HRM approaches they adopted. The relevance of intelligent career theory when applying micro-level findings at the meso-organisational level is shown.

The paper presents how the international experience of founder–managers, in turn, impacts on the HRM practices and policies that are implemented to recruit and retain international employees. The study highlights how both organisation size and founder-manager international experience influence the degree of customisation of HRM practices and policies in SMEs, specifically pertaining to the recruitment and retention of self-initiated expatriates and skilled migrant employees. The heterogeneity within the sub-categories encompassed under the umbrella label of SME is emphasised; validating our case study approach, where nuance and detail of the specific organisation can be shared.

The purpose of this paper is to propose a framework to distinguish between various types of antecedents and consequences of impulse buying. The authors tested it using a meta-analytical approach.

The authors examined 12 databases and analyzed 178 relationships in 100 articles. For the quantitative data analysis, the authors used the coefficient of correlation r as a metric to measure the effect size of the studied scope variables.

The findings of this meta-analysis demonstrated significant relation of antecedents and consequences of the impulse buying behavior, such as consumer impulsiveness (r = 0.464), materialistic consumption (r = 0.344), purchase pleasure (r = 0.270), hedonic value (r = 0.311), income (r = 0.703), gender (r = 0.150), age (r = −0.062), store atmosphere (r = 0.166), decision-making (r = 0.703) and positive emotions (r = 0.323).

This meta-analysis reviewed relationships found worldwide in the literature, expanding and improving the current knowledge. The meta-analysis identified ways that research on impulse buying is lacking and presented suggestions for the elaboration of new studies to allow future researchers to better define their agendas.

This meta-analysis brings important questions, such as impulse buying behavior is associated not only with consumer impulsiveness but also with materialistic consumption.

This research tested the impact of the antecedents and consequences of impulse buying and presented important results through this meta-analytical review. This meta-analysis contributes to the marketing literature, with a set of empirical generalizations, including relationship coefficients and calculated fail-safe numbers.

This study aims to identify the propensity for clients (legal and natural persons) to adopt peer-to-peer (P2P) short-term insurance policies as opposed to traditional and/or centralized short-term.

In this paper data was collected through a survey of 102 sampled short-term insurance clients using convenience sampling. The TAM2 questionnaire was adapted to evaluate the intention to adopt a P2P insurance policy.

The findings of this study shed light on the factors influencing the adoption and (dis)continuation of short-term insurance products, both traditional and digital, among South African consumers. The results demonstrate that perceived usefulness, ease of use, trust, risk perception and subjective norm play crucial roles in individuals' intention to use or (dis)continue the use of these insurance products.

The study's findings provide actionable insights for practitioners in the short-term insurance sector, with a focus on marketers and e-commerce professionals. These insights emphasize the need to prioritize user-friendly design and trust-building measures in the development of P2P insurance systems. Additionally, practitioners should consider harnessing the power of social influence and carefully balancing innovative features with familiarity in their marketing efforts. These strategies are poised to enhance the adoption and competitive positioning of P2P insurance solutions amidst the evolving landscape of digital transformation.

This study makes a substantial contribution by employing the technology acceptance model (TAM) in a novel and unconventional manner. It not only explicates the intricate dynamics governing the adoption and discontinuation of short-term insurance products, encompassing both conventional and digital alternatives, within the South African consumer milieu but also extends its purview to infer the reasons behind the limited widespread adoption of the digital counterpart, despite its superior value proposition compared to the traditional offering. The findings elucidate the critical determinants shaping individuals' decisions in this dynamic market segment. This research enhances the global discourse on insurance adoption with a unique South African perspective and furnishes insurers and marketers with empirically grounded insights to optimize their strategies and cultivate substantive connections with their target demographic.