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This study aims to analyze the structure and trend in employment in the Indian economy between 1980-8081 and 2015-2016.

Use of India KLEMS data set. Estimate growth rate of employment and discuss employment prospects using “Point” employment elasticity.

Whilst India’s GDP growth rate has been quite impressive since the reforms of 1991, the rate of employment growth, especially in the recent period of 2003-2015, has been quite slow (1 per cent) with low employment elasticity (0.1). The pattern of employment growth has also been imbalanced with slow rate of employment growth in manufacturing and rapid growth rate in the construction sector. India now also has low labour force participation rate and a large share of informal employment in the economy.

The limitation is the lack of reliable data on employment for the recent period.

With overall low employment elasticity, India would have to explore sectors where more employment opportunities could be created.

India has to create not only more jobs but also “good” jobs.

The India KLEMS data provide a time series for employment, which has been used in this paper to find “Point” elasticity instead of arc elasticity of employment and is an improvement over existing employment elasticity estimates.

India depicts the picture of severe social stringencies keeping girls away from attending school education due to the harsh reality of early child marriage and denial of aspirations of girl students in Indian society. The gender disparity in school educational attainment is evident as the figures of girls' enrollment in comparison to boys' enrollment at higher secondary stage of education in India always turn lower. In this context, measurement of technical efficiency (TE) is important because existence of technical inefficiency implies that one cannot produce maximum amount of output, given the resources, which can be interpreted as the penalty that the system is paying, and there is also the need to find out the relation between TE and gender inequality. The chapter contributes to the literature by (i) in the first stage estimating output-oriented TE of Indian higher secondary education for the period 2010–2011 to 2015–2016, using nonparametric Data Envelopment Analysis, for general category states and (ii) in the second stage, using the estimated TE scores from the first stage, and the regression analysis establishing the positive impact of the girls' enrollment relative to boys' on the resulting TE and hence the positive role of gender equality in enrollment on enhancing TE. The favorable role of (1) “government expenditures on education (as a ratio to aggregate expenditure for the state),” “proportion of para teachers” and the adverse role of (2) “percentage of schools without girl's toilet” and “percentage of schools without building,” in determining TE of Indian higher secondary education are evident.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

Using 13 major Indian state-level data of the rural sector, covering the period 2004–2005 to 2011–2012 and by estimating a simultaneous-panel model employing Baltagi's Instrumental-Variable EC2SLS estimation method, this chapter contributes to the literature by establishing: (i) the simultaneous dependence between female labor force participation rate (FLFPR) and female health status as measured by female life expectancy (FLE), (ii) the negative impact of outdoor air pollution as measured by prevalence of SPM, SO₂, and NO₂ on FLE, and (iii) the interaction among different demographic factors in determining both FLFPR and FLE. The interaction effect of air pollution with (i) economic growth and (ii) poverty (POV) on FLE is negative implying that the partial effect of a change in growth (POV) depends on air pollution level. Thus reduction in air pollution will increase FLE and hence FLFPR, as the simultaneous positive dependence between FLFPR and FLE is supported. The interaction effect of women's political power and education on rural FLFPR is significant and nonlinear with positive marginal effect. Thus the partial effect of a change in women's political power on FLFPR will in turn depend on level of education and vice versa. The positive impact of other demographic factors like (i) education, (ii) female leader, (iii) POV, and (iv) urbanization on FLFPR and (a) education, (b) female household head, (c) female leader, (d) sex ratio, and (e) growth on FLE are apparent. However, the household size significantly and negatively affects FLFPR.

This study attempts to discover effective strategies for mobile commerce applications (apps) to grow their consumer base by releasing app strategic updates. Drawing on the landscape search model from strategy research, this study conceptualizes mobile app update strategy as three interdependent decisions, i.e. what business elements are changed in an app strategic update, how substantial the changes are and when strategic updates are released relative to the competitive environment.

Using a field data set of 1,500 strategic updates of seven rival apps in the mobile travel market, this study integrated fuzzy set qualitative comparative analysis (fsQCA) with econometric analysis to analyze how app strategic update decisions interdependently influence app performance.

This study identified three effective and one ineffective mobile app update strategies from the mixed-method analysis, which verified the complex interdependency of app strategic update decisions. A general takeaway from these strategies is that a complex strategy problem on the mobile platform must be solved with respect to the constraints and capabilities of mobile technology.

This study moves beyond a linear view of the relationship between app update frequency and app performance and provides a holistic view of how and why app strategic update decisions mutually influence one another in their impact on app performance. This work makes contributions by identifying interdependency as a conceptual bridge between strategy and mobile app literature and developing an empirically testable version of the landscape search model.

In online exchange platforms' sponsored search advertising, the array of product quality signals within a keyword search results list plays a crucial role in shaping buyers' purchasing decisions. This research seeks to explore the impact of various quality signals – namely, ranking position, seller reputation and product price – on ad clicks. Additionally, it examines the role of keyword attributes, such as specificity and popularity, in modulating the effects of these quality signals on advertising clicks.

A total of 5,763 effective data points were collected from a leading B2B electronic platform company, and we employed negative binomial regression with Heckman correction methods to test the hypotheses.

The results indicate that in online exchange platforms, search ad clicks are significantly and positively affected by displayed signals such as ranking position, seller reputation and product price information. Notably, a U-shaped relationship emerges between product price and ad clicks. Furthermore, keyword specificity and popularity distinctly moderate the impact of these displayed signals on ad clicks within online exchange platforms.

This paper addresses the gap in existing research on search advertising by methodically analyzing the impact of various signals displayed in search results and how keyword attributes moderate ad clicks, all through a signaling theory lens.

As an increasing number of users have acquired information across the web and mobile platforms for social question and answering (Q&A), it is of interest to explore whether there are differences in social Q&A usages between the two platforms. The purpose of this paper is to compare web and mobile platforms of a social Q&A service from the user’s perspective in terms of three dimensions, namely, demographics, individual-based constructs, and information-based constructs.

Because Zhihu.com is one of the most popular social Q&A sites in China, the authors used online questionnaires to investigate its users’ perceptions of these three dimensions. From January to March 2016, the authors obtained 278 valid responses in total through snowball and convenient sampling. Collected data are analyzed through descriptive statistics and inferential statistics.

The results indicate that there exist significant differences between web users and mobile users on Zhihu.com in terms of gender, affinity, and information seeking. More specifically, compared to the male users, more female users rely on the mobile platform to access the information service; mobile users perceive higher affinity with Zhihu.com than web users; and mobile users perceive higher information-seeking intention than web users do.

Regarding the theoretical aspect, this study proposes a conceptual framework for comparison between the web and mobile platforms of social Q&A from the user’s perspective. Regarding the practical aspect, the comparative results of this study could give social Q&A service providers useful information about users’ differences between web and mobile platforms of social Q&A services.

Unmanned aerial vehicles (UAVs) have a wide variety of applications such as surveillance and search. Many of these tasks are better executed by multiple UAVs acting as a group. One of the main problems to be tackled in a high‐density UAV traffic scenario is that of collision avoidance among UAVs. The purpose of this paper is to give a collision avoidance algorithm to detect and resolve the conflicts of projected path among UAVs.

The collision avoidance algorithm developed in the paper handles multiple UAV conflicts by considering only the most imminent predicted collision and doing a maneuver to increase the line‐of‐sight rate to avoid that conflict. After the collision avoidance maneuver, the UAVs fly to their destinations via Dubins shortest path to minimize time to reach destination. The algorithm is tested on realistic six degree of freedom UAV models augmented with proportional‐integral controllers to hold altitude, velocity, and commanded bank angles.

The paper shows, through extensive simulations, that the proposed collision avoidance algorithm gives a good performance in high‐density UAV traffic scenarios. The proposed collision avoidance algorithm is simple to implement and is computationally efficient.

The algorithm developed in this paper can be easily implemented on actual UAVs.

There are only a few works in the literature that address multiple UAV collision avoidance in very high‐density traffic situations. This paper addresses very high‐density multiple UAV conflict resolution with realistic UAV models.

The role of affective states in consumer behaviour is well established. However, no study to date has examined online affective states empirically as a basis for constructing typologies of internet users and for assessing the invariance of clusters across national cultures. This paper aims to address this issue.

Four focus groups were carried out with internet users to adapt a set of affective states identified from the literature to the online environment. An online survey was then designed to collect data from internet users in four Western and four East Asian countries.

Based on a cluster analysis, six cross‐national market segments are identified and labelled “Positive Online Affectivists”, “Offline Affectivists”, “On/Off‐line Negative Affectivists”, “Online Affectivists”, “Indistinguishable Affectivists”, and “Negative Offline Affectivists”. The resulting clusters discriminate on the basis of national culture, gender, working status and perceptions towards online brands.

Marketers may use this typology to segment internet users in order to predict their perceptions towards online brands. Also, a standardised approach to e‐marketing is not recommended on the basis of affective state‐based segmentation.

This is the first study proposing affective state‐based typologies of internet users using comparable samples from four Western and four East Asian countries.