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Attaining high response rates in survey‐based supply chain management (SCM) research is becoming increasingly difficult, but small samples can limit the reliability and validity of empirical research findings. The purpose of this article is to analyze the status quo and provide a discussion of methodological issues related to the use of small samples in SCM research.

An in‐depth review of 75 small sample survey studies published between 1998 and 2007 in three journals in the field that frequently publish survey‐based research papers (TJ, IJPDLM, and JBL) was conducted, and key characteristics of these studies were compared with the characteristics from 44 small sample survey studies published in leading operations management (JOM) and management (AMJ) journals.

The review of papers published in TJ, IJPDLM, and JBL shows that small samples are frequently used in SCM research. This study provides an overview of current practices, opportunities for improvement, and a number of specific recommendations that may help increase the analytical rigor of (future) survey‐based studies that rely on small samples.

The recommendations provided in this article can greatly benefit researchers in the field of SCM. By following these proposals, the reliability and validity of research findings will be increased, researchers will be better equipped to investigate interesting questions where small samples are the norm rather than the exception (e.g., the study of dyadic supply chain relationships), and important and valid contributions to the theory and practice of SCM will be generated.

The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly.

First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated.

A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources.

With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing.

A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.

The purpose of this research is to show that while mainstream finance for small businesses has been researched, hard to reach segments of the UK owner/manager population have eluded empirically rigorous investigation. The authors investigate the financing preferences of owner/managers in small ethnic minority businesses in the UK and examine their access to both formal and informal finance as well as the use of personal funding networks. The emergent results are compared with the findings from a matched “control sample” of white small business owner/managers.

Identical, in‐depth, face‐to‐face interviews were used with a sample of ethnic minority small business owner/managers and a matched control sample of white respondents in the West Midlands region of the UK.

Family and close associate networks were very important for the support of both ethnic minority and white owner/managers. All the respondents required loans from banks and other financial institutions, both at the start‐up stage and in subsequent years. For the ethnic minority owner/managers, the initial importance of financial institutions declined over the years. In contrast, in the control sample, institutional borrowing needs increased considerably. Ethnic minority owner/managers showed a preference for less intrusive and more “user friendly” financing options that allow them to remain in full control of their businesses.

Caution is advised in the use and generalisation of results emerging from qualitative research that involves small samples of respondents chosen from a restricted area of the UK.

The research shows the importance of “user‐friendly” financing options for owner/managers.

To solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.

Based upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.

The new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.

This research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.

The purpose of this paper is to estimate the cost of the internal control audit for small firms and assess whether the costs are scalable or, alternatively, whether they are higher in relation to firm size for small firms than for larger firms.

The method estimates the Section 404 audit fee premium for companies that became accelerated filers for the first time in 2006 and 2007. The authors use the estimated audit fee premium as a proxy for the premium that non‐accelerated filers would have to pay if they were required to obtain an internal control audit.

The main finding shows that the Section 404(b) estimated cost of the internal control audit divided by assets are significantly higher for non‐accelerated filers and first‐time filers than for those of larger firms.

One limitation of the study is that, while it shows that the costs of the Section 404 audit are not scalable to firm size, it does not prove that the costs of the audit exceed the benefits for non‐accelerated filers.

The finding that the costs are proportionately higher for small firms provides some evidence supporting the decision to permanently exempt non‐accelerated filers from Section 404(b).

The results show that smaller firms pay proportionately more for the Section 404 internal control audit than larger firms. This suggests that the revised AS no. 5 did not succeed in making the internal control audit completely scalable to firm size.

Despite the growing significance afforded to small and medium‐sized enterprises (SMEs) in contemporary Britain, much of the industrial relations (IR) literature in this country is based on research undertaken in large organisations. The minority of IR studies that are focused on smaller firms tend to be prescriptive and their results polarise into either the “small is beautiful” or the “bleak house” perspective and involve relatively small target samples. As a result, there is a paucity of quantitative research that could be generalised across this important sector of the British economy. The research (1998‐2000) on which this article is based originated from a perceived need to redress this imbalance. Quantitative and qualitative data were collected from a target sample of 6,000 organisations. Preliminary results indicate that the majority of small business owner/managers in the sample exhibited personalised and mostly informal management styles. It emerges that IR in this type of business can be as varied as the characteristics, personalities and preferences exhibited by the respondents in the research sample.

By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems.

In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true.

The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.