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The purpose of this study is to review the literature on strategic management in Africa with special emphasis on how strategy constructs have been measured and present a roadmap to help improve strategy research in Africa.

A content analysis of empirical research on strategic management published in journals using data from Africa from 2000 to 2013 is conducted to examine construct measurement practices.

The findings indicate that the average sample sizes in strategy research in Africa is not large as strategy research in general, and have low statistical power. While the studies rely heavily on single-indicator measures, there were also several studies using scale or multiple measures that report reliabilities.

Limitations of the research include small number of studies used, inability to examine journal effects’ of the findings due to few numbers of papers from many of the journals, and lack of examination of the influence of the context and topical areas of the articles on the use of the construct measurement techniques.

The study provides information about the use of construct measurement techniques and power analysis in strategy research in Africa. It further encourages the use of larger sample sizes, the examination of power, and more focus on variables which allow the assessment of reliabilities and validity.

Little is known about construct measurement practices of the empirical research in and about Africa in the discipline of strategic management. This chapter builds on extant research on construct measurement issues in strategic management research, but with the unique value-added contribution of focusing on the African environment where the discipline is beginning to take hold.

Understanding consumer behavior across various contexts within marketing has long been the focus of studies. Although many models are used in explaining consumers' behavior, one of these is the Model of Goal-Directed Behavior (MGB), which is becoming prominent in explaining consumers' behavior in marketing. Given its popularity, prior research on MGB has shown inconsistent outcomes regarding the casual association of MGB variables. To overcome this, the authors have adopted a meta-analytic review of the marketing studies grounded on MGB theory in examining the consumers' behavior.

The study reviewed and analyzed 611 correlations from 27 studies with 31 samples (combined sample size of 9588) using a meta-analytic structural equation modeling (MASEM) technique.

The outcomes of MASEM confirm the significance of all the proposed relationships in the MGB model. However, the attitude has shown a strong influence on desire formation among all the proposed MGB relationships. Further, past buying experience and positive anticipated emotions strongly affect desire in developed nations compared to developing nations.

The current work has considered the possibility of various recommended moderators (e.g. culture, crisis situation, sample size, method of data collection, etc.); however, the study lacks to consider the dimension of gender dominance in it. Hence, future researchers should keep it in mind while conducting similar studies. Future scholars can also perform a comparative study on MGB across the domains and subdomains to know more insights.

The current work offers a better understanding of MGB application in marketing. As this work is one of the first meta-analyses on MGB application in marketing that also considers the effect of various moderators, it thus adds knowledge to the literature on MGB in marketing. It will also help the future researchers to understand MGB as a framework and its application in marketing.

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

For central banks who study the use of cash, acceptance of card payments is an important factor. Surveys to measure levels of card acceptance and the costs of payments can be complicated and expensive. In this paper, we exploit a novel data set from Hungary to see the effect of stratified random sampling on estimates of payment card acceptance and usage. Using the Online Cashier Registry, a database linking the universe of merchant cash registers in Hungary, we create merchant and transaction level data sets. We compare county (geographic), industry and store size stratifications to simulate the usual stratification criteria for merchant surveys and see the effect on estimates of card acceptance for different sample sizes. Further, we estimate logistic regression models of card acceptance/usage to see how stratification biases estimates of key determinants of card acceptance/usage.

The purpose of this paper is to study the role of corporate governance in abnormal returns around announcements of seasoned equity offerings (SEOs) by publicly traded US firms from 2001 to 2004.

Cross‐sectional regression analysis was used to determine which variables are important to the market's reaction to the SEO, with a particular focus on corporate governance variables.

It was found that investors react more positively for firms in which different people hold the CEO and board chairman positions. Limited evidence was found that investor reaction is more positive when the board has a greater representation of outside directors, the CEO has less ownership, and the board is not too large. These findings suggest that investors react more favorably to SEOs by firms with stronger corporate governance mechanisms that reduce adverse selection or agency problems.

This paper's findings are evidence that stronger boards can reduce a firm's cost of raising additional equity capital. Originality/value – There is not believed to be any other published paper that examines the impact of corporate governance mechanisms on the reaction to SEOs with such a comprehensive sample or in post‐Enron periods.

The purpose of this paper is to counter autocorrelation by designing the chart, using warning limits. Various optimal schemes of modified chart are proposed for various sample sizes (n) at levels of correlation (Φ) of 0.00, 0.475 and 0.95. These optimal schemes of modified chart are compared with the double sampling (DS) chart, suggested by Costa and Claro (2008).

The performance of the chart is measured in terms of the average run length (ARL) that is the average number of samples before getting an out-of-control signal. Ultimately, due to the effect of autocorrelation among the data, the performance of the chart is suspected. The ARLs at various sets of parameters of the chart are computed by simulation, using MATLAB. The suggested optimal schemes are simpler schemes with limited number of parameters and smaller sample size (n=4) and this simplicity makes them very helpful in quality control.

The suggested optimal schemes of modified chart are compared with the DS chart, suggested by Costa and Claro (2008). It is concluded that the modified chart outperforms the DS chart at various levels of correlation (Φ) and shifts in the process mean. The simplicity in the design of modified chart, makes it versatile for many industries.

Both the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for chart can be developed for variable sample size and for variable sampling intervals. The optimal schemes can also be explored for cumulative sum and exponentially weighted moving average charts.

The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. The understandable and robust design of modified chart makes it usable for industrial quality control.

The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi (1985).

Although it is the extension of previous work but it can be applied to various manufacturing industries as well as service industries, where the data are positively correlated and normally distributed.

Open-Economy models are central to the discussion of the trade-offs monetary policy faces in an increasingly more globalized world (e.g., Marínez-García & Wynne, 2010), but bringing them to the data is not without its challenges. Controlling for misspecification bias, we trace the problem of uncertainty surrounding structural parameter estimation in the context of a fully specified New Open Economy Macro (NOEM) model partly to sample size. We suggest that standard macroeconomic time series with a coverage of less than forty years may not be informative enough for some parameters of interest to be recovered with precision. We also illustrate how uncertainty also arises from weak structural identification, irrespective of the sample size. This remains a concern for empirical research and we recommend estimation with simulated observations before using actual data as a way of detecting structural parameters that are prone to weak identification. We also recommend careful evaluation and documentation of the implementation strategy (specially in the selection of observables) as it can have significant effects on the strength of identification of key model parameters.

This paper proposes a new inventory model with inspection policy because in practice the received orders may contain non- conforming (NC) items. So, a buyer who receive an order from a supplier should use an inspection policy.

The inspection policy is assumed to be zero-defect single sampling. Under this policy a lot is accepted only if no defect has been identified in the inspected sample. The fraction of NC is assumed to be a random variable following a Binomial distribution and the number of NC items detected by inspection assumed to be a random variable, which follows a hypergeometric distribution. Order quantity and sample size are the two decision variables. A solution procedure is presented for the proposed model. The proposed procedure presents the optimal solution.

Numerical examples presented to illustrate the procedure outlined for the proposed model and its applicability. The results of numerical examples and comparing them with traditional EOQ model reveal that by the proposed model, the buyer could reduce total cost that shows the efficiency and validity of the proposed model.

The novelty of this paper is the new proposed model that considers inspection policy in inventory management. The proposed model determines sample size as well as order quantity to consider both subject of inventory management and quality control, simultaneously.

The purpose of this paper is to introduce a new design of the X¯ chart to catch smaller shifts in the process average as well as to maintain the simplicity like the Shewhart X¯ chart so that it may be applied at shopfloor level.

In this paper, a new X¯ chart with two strategies is proposed which can overcome the limitations of Shewhart, CUSUM and EWMA charts. The Shewhart chart uses only two control limits to arrive at a decision to accept the Null Hypothesis (H₀) or Alternative Hypothesis (H₁), but in the new X¯ chart, two more limits at “K” times sample standard deviation on both sides from center line have been introduced. These limits are termed warning limits. The first strategy is based on chi‐square distribution (CSQ), while the second strategy is based on the average of sample means (ASM).

The proposed X¯ chart with “strategy ASM” shows lower average run length (ARL) values than ARLs of variable parameter (VP) X¯ chart for most of the cases. The VP chart shows little better performance than the new chart; but at large sample sizes (n) of 12 and 16. The VSS X¯ chart also shows lower ARLs but at very large sample size, which should not be used because, as far as possible, samples should be taken from a lot produced under identical conditions. The inherent feature of the new chart is its simplicity, so that it can be used without difficulty at shopfloor level as it uses only a fixed sample size and fixed sampling interval but it is very difficult to set the various chart parameters in VP and VSS X¯ charts.

A lot of effort has been expended to develop the new strategies for monitoring the process mean. Various assumptions and factors affecting the performance of the X¯ chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and performance of the proposed chart may be studied.

The research findings could be applied to various manufacturing and service industries as it is more effective than the Shewhart chart and simpler than the VP, VSS and CUSUM charts.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.