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The purpose of this paper is to identify reasons as to why firms operate informally, and to explore the rationalisation of not moving towards formalisation.

Building on empirical data collected through interviews with the entrepreneurs of the informal service enterprises of Delhi and peripheral areas, this paper presents the results of the survey conducted in 2017. By using the analytical tool developed by I. Bross, the authors have identified the most significant contributing factors to informality.

In the research, the authors establish that competition and lack of awareness are the most dominant reasons due to which informal firms are hesitant in moving towards formalisation. For successful transition towards formalisation, a “new pact” between the workers, enterprises and governments needs to be developed, based on capacity building, productivity gains, enabling business environment, empowerment and entitlements to social and economic rights.

The research is limited only to informal service enterprises located in Delhi, the national capital of India, and the peripheral areas.

By identifying the most dominant factors, focussed steps can be taken to reduce the size of the informal sector.

The informal service enterprises are not a widely explored community by researchers and policy makers. This sector can employ more people with less investment, and hence requires intensive study. The use of RIDIT approach to rank the identified factors due to which the firms do not move towards formalisation is the novelty of this work.

The purpose of this paper is to study the characteristics of the workers in the informal economy and explore the reasons for workers migrating from rural to urban area. The authors also explore and enumerate various reasons why the migrants choose to work informally and study whether internal migrants treat this sector as temporary or transitory before moving to the formal sector. The authors reconnoitre the issues in coverage of factors in unregistered service, this research is carried out at a smaller scale of operations of the service enterprises having a minimum of three (or more) employees, which poses significant issues in the enumeration. This work further emphasises on the reasons why people migrate and choose the informal sector (IS) and to estimate the contribution of technology towards productivity of this sector.

Building on empirical data collected through interviews with the migrants, who are now part of the informal service enterprises of Delhi and peripheral areas, this paper presents the results of a survey conducted in 2017. This research is based on appropriate scale driven by the instrument of choice by the sampling of units. Data were collected by conducting field survey using structured questionnaire. By performing the estimation at the unit rather than the industry level, the authors reduce difficulties of mis-measured output and inputs, thus, potentially obtaining a more accurate estimate of technologies contributions towards the firm productivity.

The small service enterprises are making poorer value addition towards measured productivity due to the factors such as lack of equipment or technical know-how. The authors find that marginalisation thesis holds true only partially and the rest are in this sector by choice, considering it as an opportunity. Maximum number of entrants had been attracted by opportunities in this sector itself; actual and potential mobility from the informality towards formalisation is quite low; education is one of the important determinants for entrepreneurs shifting from informality towards formalisation.

The research is limited only to informal service enterprises located in Delhi, the national capital of India and the peripheral areas.

By identifying the factors, proper policy measures can be designed which will be in a focused direction to reduce the size of the IS and to improve the working condition of the migrants who are part of this sector. The estimation is at the unit level using primary data.

The estimation is at the unit level using primary data, the research contributes to the literature on informal service sector, and this sector needs more intensive large-scale studies in order to design policies which can result in betterment of the society as a whole, benefiting the segment that needs immediate attention from government and society as well.

This study is based on the development of predictive classification for the success of a venture capital (VC) deal derived from both qualitative and quantitative indicators.

Decision tree analysis has used for devising the success model of VC deal. Various deal characteristics are considered in this study as the observable component of success.

The finding of this analysis indicates that the success of the deal does not only depend on the final outcome like post company valuation (POST_COMP), realised revenue (RREV) but also depends on various observable contractual characteristics like syndication, use of convertible security and ownership percentage with some noticeable deal features.

This study increases the further scope of study on a contractual mechanisms such as allocation of cash flow right and control right in the deal contract between venture investor and entrepreneur firm. This could give a better understanding of success path of a venture deal.

This study has attempted to derive a performance model based on observable attributes of a VC deal.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.