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Article

Eleanor Shaw

Provides a detailed description of the qualitative research process experienced by the author when undertaking doctoral research. Recognising that there are few articles…

Abstract

Provides a detailed description of the qualitative research process experienced by the author when undertaking doctoral research. Recognising that there are few articles to guide the qualitative small firm researcher, it is the intention to provide a detailed account of the process and decisions involved when undertaking qualitative small firm research. From a discussion of the factors that convinced the author of the appropriateness of a qualitative approach, to a consideration of the outcomes generated, this paper guides the reader through the process of qualitative data collection and inductive analysis experienced by the author. In so doing, it demonstrates the value of using such an approach when undertaking small firms research.

Details

Qualitative Market Research: An International Journal, vol. 2 no. 2
Type: Research Article
ISSN: 1352-2752

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Book part

Stefano Fenoaltea

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first…

Abstract

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.

Details

Research in Economic History
Type: Book
ISBN: 978-1-78635-276-7

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Article

Biao Mei, Weidong Zhu, Yinglin Ke and Pengyu Zheng

Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

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Article

Huifeng Pan, Man-Su Kang and Hong-Youl Ha

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this…

Abstract

Purpose

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is to examine both how these data affect the credit evaluations of small businesses and how financial managers use them to stabilize their risks.

Design/methodology/approach

Using data from 97,889 data points for normal guarantees and 1,678 data points for accidents in public funds, the authors explore the effects of trade area grades as well as the superiority of the use of big data when evaluating credit ratings for small businesses.

Findings

The results indicate that the grade information of trade areas is useful in predicting accident rates, particularly for small businesses with high credit scores (AAA-A). On the other hand, the accident rates of small businesses with low credit scores increased from 3.15-16.67 to 3.20-33.3 percent. These findings demonstrate that accident rates for the businesses with high credit scores decrease, but accident rates for businesses with low credit scores increase when using the grades of trade areas.

Originality/value

The authors contribute to the literature in two ways. First, this study provides one of the first investigations on information on trade areas through public financial perspectives, thereby extending the financial risk and retail literature. Second, the current study extends the research on the credit evaluation of small businesses through the big data application of real transaction-based trade areas, answering the call of Park et al. (2012), who recommended an exploration of the relationship between business start-ups and financial risk.

Details

Management Decision, vol. 55 no. 9
Type: Research Article
ISSN: 0025-1747

Keywords

Content available
Article

Liam Fahey

Defines a useful context for the concept of market insight and explains how “small data” can illuminate new pathways to such insight and potentially to better market strategies.

Abstract

Purpose

Defines a useful context for the concept of market insight and explains how “small data” can illuminate new pathways to such insight and potentially to better market strategies.

Design/methodology/approach

Using an illustrative case the article provides a guide to transforming “small data” into important strategy shaping insights.

Findings

By paying careful attention to such small data opportunities, fully assessing their inferences and vetting them thoroughly, strategist and marketers can discover insights that Big Data cannot deliver.

Originality/value

Small data as a source of learning is not just neglected but disparaged in many organizations. The importance of such small data escalates when we recognize that they frequently serve as the early indicator of marketplace change that may not be observable by other means.

Details

Strategy & Leadership, vol. 47 no. 3
Type: Research Article
ISSN: 1087-8572

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Article

Wenguang Yang, Lianhai Lin and Hongkui Gao

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Abstract

Purpose

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

Small data can net huge gains for organizations attempting to boost competitive advantage, or find a strategic pathway not yet discovered.

Originality/value

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Details

Strategic Direction, vol. 35 no. 9
Type: Research Article
ISSN: 0258-0543

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Article

Renze Zhou, Zhiguo Xing, Haidou Wang, Zhongyu Piao, Yanfei Huang, Weiling Guo and Runbo Ma

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are…

Abstract

Purpose

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

Design/methodology/approach

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

Findings

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

Practical implications

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

Originality/value

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

Details

Anti-Corrosion Methods and Materials, vol. 67 no. 1
Type: Research Article
ISSN: 0003-5599

Keywords

Content available
Article

Yingjie Yang, Sifeng Liu and Naiming Xie

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part…

Abstract

Purpose

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

Design/methodology/approach

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Findings

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

Research limitations/implications

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

Practical implications

The proposed model has the potential to avoid the mistake from a misleading data imputation.

Social implications

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

Originality/value

This is the first time that the whole data analytics is considered from the point of view of grey systems.

Details

Marine Economics and Management, vol. 2 no. 2
Type: Research Article
ISSN: 2516-158X

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Article

Normah Omar, Zulaikha ‘Amirah Johari and Malcolm Smith

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in…

Abstract

Purpose

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia.

Design/methodology/approach

Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN.

Findings

The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting.

Originality/value

The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.

Details

Journal of Financial Crime, vol. 24 no. 2
Type: Research Article
ISSN: 1359-0790

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