Search results

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.

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.

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.

To develop, test and implement a sampling strategy for equipment auditing for a Fortune 100 company.

Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations.

Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre‐ and post‐audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided.

The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre‐ and post‐audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes.

The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Fortune 100 company, and are generally applicable to equipment and inventory auditing when high correlation between pre‐ and post‐audit equipment is expected.

This paper provides a practical and useful regression‐based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.

The purpose of this study is to integrate findings of empirical studies regarding the effect of total quality management (TQM) on competitive advantage. This purpose is to support building a theoretical model of TQM and its components. These components are: top management commitment/leadership, teams, culture, training/education, and process efficiency; they are grounded in the work of Deming and deduced from three other models offered by Dean and Bowen and Reed et al.

This study employed a meta‐analysis to synthesize results of a sample of 51 studies into, effectively, one database. The meta‐analysis approach is used to establish external validity for the theoretical model of TQM used in the paper. The sample includes studies that were conducted in different countries to provide a comprehensive investigation.

Each individual component of TQM was associated with competitive advantage, that these associations each explain roughly 60 percent of the variability in competitive advantage, and that a 1 point change in an average component score (1‐5 Likert scale) results in at least a 0.1 point change in competitive advantage.

The strong correlations between the five components, coupled with the limited sample size, made it impossible to fit a competitive advantage explanation model that included all five components with any statistical significance. Thus, it was not possible to determine the relative impacts of the five components on competitive advantage. Moreover, these limitations made the impact of leadership relative to other variables indeterminate, even in two independent variable models.

Despite the modest findings, this study provides a link between the theory and practice of TQM efforts and provides direction for future research.

It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases. The purpose of this paper is to compute the level of correlation among the observations of the weights of tablets of a pharmaceutical industry by using modified X-bar chart.

The design of the modified X-bar chart is based upon the sum of χ2s, using warning limits and the performance of the chart is measured in terms of average run lengths (ARLs). The ARLs at various sets of parameters of the modified X-bar chart are computed; using MATLAB software at the given mean and standard deviation.

The performance of the modified X-bar chart is computed for sample sizes of four. ARLs of optimal schemes of X-bar chart for sample size of four are computed. Various optimal schemes of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.00, 0.25, 0.50, 0.75 and 1.00 are presented in this paper. Samples of weights of the tablets are taken from a pharmaceutical industry and computed the level of correlation among the observations of the weights of the tablets. It is found that the observations are closely resembled with the simulated observations for the level of correlation of 0.75 in this case study. The performance of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.50 and 0.75 is also compared with the conventional (Shewhart) X-bar chart and it is concluded that the modified X-bar chart performs better than Shewhart X-bar chart.

All 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 modified X-bar chart can also be used for other industries; where the manufacturing time of products is small. This scheme may also be used for any sample sizes suitable for the industries

The optimal scheme of modified X-bar chart for sample size (n) of four is used according to the computed level of correlation in the observations. The simple design of modified X-bar chart makes it more useful at the shop floor level for many industries where correlation exists. 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 used.

The design of modified X-bar chart uses very less numbers of parameters so it can be used at the shop floor level with ease. 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 and service industries; where the data are correlated and normally distributed.

The purpose of this study is to investigate empirically factors influencing Kuwaiti companies to disseminate their financial reports on the Internet. A regression model is estimated using logit analysis for a sample of 179 Kuwaiti companies listed on the Kuwait Stock Exchange for 2007 to test the hypotheses of the study. The study’s results indicate that an Internet financial report is significantly influenced by the auditor type, company size and industry type. The results, however, do not provide evidence of a significant relation between Internet financial reports and any of the corporate governance attributes that were examined in this study.