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This research examines micro‐enterprises pursuing gradual growth. While very little research has been targeted specifically at the growth of micro‐enterprises, there are a host of possible influencing factors suggested by the rather broader small business literature. Less research has attempted to integrate the factors that influence growth of small firms into some form of model. Those models that were found had a number of shortfalls when it came to understanding the development of micro‐enterprises. A framework has been developed through this research that addresses these shortfalls. First, it has targeted specifically gradual growth micro‐enterprises; secondly, it is rigorously under‐pinned through empirical research; thirdly, it attempts to comprehensively cover the range of factors that influence development; fourthly, it focuses on the complex interaction of factors that may influence development. The research findings and implications are presented in two parts. Part 1 develops an empirically verified framework that explains how growth is influenced by a myriad of interacting factors. This leads to a discussion of the policy implications of the framework. Part 2 is presented in the next edition of the Journal of Small Business and Enterprise Development (JSBED) and will explore the managerial implications of the framework. This will provide a diagnostic toolkit to help micro‐enterprise owner‐managers and advisers pursue growth. The paper is derived from research conducted initially for the submission of a PhD thesis at the University of Brighton (Perren, 1996).

Presents an inductive machine learning algorithm called CLIP3 (Cover learning using integer programming). CLIP3 is an extension of the CLILP2 algorithm. CLIP3 generates multiple rules for a given concept from two sets of discrete attribute data. It combines the best concepts of tree‐based and rule‐based algorithms to produce a highly reliable machine‐learning algorithm. The algorithm is run on the benchmark “MONK′s data sets”. Compares the results of standard machine learning algorithms such as the ID and AQ families of algorithms. The algorithm is also run on the breast cancer data set and the results are compared with C4.5 algorithm results.

THE contents of this issue incline very much towards the sphere of building and construction. There is a particular reason for this. The Advisory Service for the Building Industry, under its new director, Edwin Wilde, has organized a seminar for directors and chief executives of leading firms in that industry. For two days, on 9 and 10 January, they will be subjected to an intensive educational course in work study from experts.

The purpose of the paper is to segment and profile the Indian shoppers in the context of organic foods in India. It proposes to use a healthy lifestyle (HL) as a segmenting variable and to use a factor-cluster analysis approach to achieve the same. The current study is expected to add a substantial base to the segmentation literature in marketing.

Food stores in Indian metropolitan city Chennai are sampled, and data is collected in the form of a mall intercept survey method. In total, 441 usable structured questionnaires are filled by the respondents which are subjected to suitable statistical analysis.

Three significantly different consumer segments emerged from the given sample of respondents, which shows uniqueness concerning consumer’s, HL features, demographics and the variables of the theory of planned behavior (TPB).

Clustering method used to segment the potential shoppers of organic foods is an exploratory technique only. It cannot be treated or generalized to the population like those of inferential techniques. The researcher suggested testing the same with a larger sample size and in a different context. It is limited to urban and suburban facets of the metropolitan city in India.

The study will be helpful to marketers and decision makers to target the potential organic foods consumers.

IN the investigation of stability problems in physical science it is essential that ‘stability criteria’ be available to ensure that the characteristic equation, which is usually an algebraic polynomial equation, is such that the roots, if real, are negative; and, if complex, have their real parts negative.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

Discusses the introduction of PASSPORT software and the effect it has on ILL work. Suggests eight function key macros designed to make use of PASSPORT easier. Describes what each macro suggested does, and gives step‐by‐step instructions to activate each.

This study aims to analyse the impact of the country of origin of wines on cognitive processing and preferences through brain responses for consumers from the perspective of gender and the level of involvement.

A wine tasting experiment was performed using electroencephalography (EEG). The sample consisted of 40 students from an important Brazilian university. In the first group, the participants tasted two glasses of wine with Brazilian then French origins. In the second group, the participants only tasted one glass of wine, without the origin information. The wine was the same in both groups and from Brazilian origin. Throughout the tasting process, participants had their brain responses recorded via the EEG.

The main findings suggest that the country of origin did not have a significant influence on cognitive processing or preferences for consumers in general, neither for women nor consumers of high involvement. For men, there was greater cognitive processing for Brazilian wines. However, there was no preference for men among the origins of wines. For consumers with low involvement, there was a difference in cognitive processing, presenting a greater value for Brazil and without origin information. Also, for low-involvement consumers, a greater preference for wine from France was seen.

This study presents a new contribution to the literature by analysing the cognitive processing and preferences through brain responses for consumers from the perspective of gender and the level of involvement.

The purpose of this paper is to identify and rank the contextual relationship among the quality management (QM) constructs for manufacturing and its related service organizations providing integrated value bundles, which has got limited research attention.

The QM constructs of manufacturing and services are selected by performing the citation analysis. Studies related to manufacturing and service are selected by reviewing their citations index in the three major research databases namely ProQuest, Scopus and Google Scholar. The subjective responses from both industry and academia are taken in order to gain better understanding of the contextual relationship among the QM constructs. Finally, an integrated model is proposed using Micmac analysis and Interpretive Structural Modeling (ISM).

The citation analysis lead to identification of 12 QM constructs: top management commitment, supplier relationship management, human resource management, strategic focus, customer focus, quality of information sharing, process management, servicescape, employee involvement, service quality, supply chain flexibility and customer satisfaction. The expert opinions of executives in the automotive industry and academia resulted in the development of contextual relationships among the identified QM constructs for the development of an ISM model, which is a major contribution of this study.

The unique focus of this study is on analyzing the contextual relationship among QM aspects in manufacturing and services as clubbed offering. The researchers in the existing literature have so far differentiated between manufacturing and services but this study integrates the QM constructs for manufacturing and its associated services through expert feedback and proposes an ISM model.

The effectiveness of combination monetary feedback rule and fiscal policy under rational expectations is examined. The fiscal policy takes into account not only a progressive tax structure but also government expenditure. The model consists of IS and LM relationships and a Lucas supply function. The model is solved analytically. Both the progressive tax structure and the combination monetary feedback provide an automatic but not immediate adjustment to reduce the expectation error. Therefore the behaviour of real output is influenced not only by the disturbance terms but also by the policy parameters of the progressive tax structure and the combination monetary feedback rule.