Collaboration and innovation: An empirical study of Indian technological enterprises

In 2010, the Indian government declared 2010–2020 to be the ‘Decade of Innovation’ and established the State Innovation Councils and Sectoral Innovation Councils to encourage and facilitate innovation by technological firms. In this paper, we study the relationship between collaboration and innovation in a cross‐section of Indian firms, paying particular attention to the impact of the innovation councils. Our results suggest that domestic collaborations have an important impact on firm innovation, as do the innovation councils, but that the impact of the councils is less for firms that collaborate.     

Personality traits,basic individual values and GMO risk perception of twitter users

Abstract

The present study investigates how the most foundational factors to individual differences – personality traits and personal values – affect the perceived safety of genetic modification and their relative importance. Publicly available communication data from 522 Twitter accounts discussing genetically modified foods and their safety was processed in two steps. First, accounts were categorized by the researchers as viewing GM foods as either safe or not safe. Second, using the IBM Watson platform, the Twitter communication data were subjected to lexical analysis to assign scores according to the Five Factor Model for personality traits and Schwartz’s basic individual values to the individual accounts. Logistic regression analyses were performed to determine how perceived GM food safety is linked to personality traits and individual values. Although both traits and values significantly contribute to explaining GM attitudes, personality traits strongly moderate the effect of individual values on risk perception.     

TFP growth in Chinese cities: The role of factor-intensity and industrial agglomeration

This article estimates agglomeration effects via calculating EG (Elilsion & Glaeser) and TFP growth (Total Factor Production) by considering the undesired output of the industrial enterprise database and the entropy weight method. Using panel data of 207 county-level cities in China and 28 two-digit manufacturing industries from 2003 to 2013 based on SIC codes, this paper analyzes the relationship between agglomeration and TFP growth through the smooth transition model under different regions and factor-intensity. The results are as follows. (1) A negative relationship appears in manufacturing productivity. The agglomeration effect changes to the crowded effect. Environmental pollution is also generated by transportation and inadequate pollution treatment technology. (2) The excessive agglomeration phenomenon of developed areas (eastern region) is less than the less developed areas (central and western regions). (3) Resource-intensity industries present two thresholds that indicate complex regional features. For various intensive industries in different regions, the relationship between GML and agglomeration is different. High agglomeration does not always promote TFP growth. (4) At different levels of urban industrial agglomeration, the influences of efficiency change and technical change on GML are different. Overall, moderate agglomeration in all regions helps promote economic development.     

The financial advice puzzle: The role of consumer heterogeneity in the advisor choice

We investigate motivational drivers of financial advisor use, accounting for investor heterogeneity, with the goal of helping institutions increase the use of financial advisor services. The results from a latent class choice model reveal two distinct segments that differ in their approach to the financial advice decision. While higher levels of risk tolerance, trust, and self-efficacy increase financial advice use for both segments, albeit at much higher propensities for Segment 1, personality only matters for Segment 1. Moreover, their regulatory focus differs with Segment 1 being promotion and Segment 2 being prevention focused. Using these results, we offer suggestions for marketing strategies.     

No‐arbitrage implies power‐law market impact and rough volatility

Market impact is the link between the volume of a (large) order and the price move during and after the execution of this order. We show that in a quite general framework, under no‐arbitrage assumption, the market impact function can only be of power‐law type. Furthermore, we prove this implies that the macroscopic price is diffusive with rough volatility, with a one‐to‐one correspondence between the exponent of the impact function and the Hurst parameter of the volatility. Hence, we simply explain the universal rough behavior of the volatility as a consequence of the no‐arbitrage property. From a mathematical viewpoint, our study relies, in particular, on new results about hyper‐rough stochastic Volterra equations.     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Heterogeneity and netting efficiency under central clearing: A stochastic network analysis

This paper examines the effect of heterogeneity in clearing members’ exposure management practices under central clearing. Our network model specifies the dynamics of prenetted interbank exposures to shape interdependent exposure distributions beyond normality. Employing over-the-counter derivatives market data from the U.S. Office of the Comptroller of the Currency, our simulation results indicate that heterogeneity in bank-to-bank exposure dynamics is systemically desirable, while the entire system benefits more from the central clearing in more homogeneous environments. Policymakers should incentivize individual clearing members to enhance resiliency and stability in counterparty exposure management to maximize netting efficiency under central clearing.     

Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.     

Becoming a tree when I will be dead? Why not! Generation X,Y and Z,and innovative green death practices

Within the theoretical field of ethical consumption, the study focuses on consumer behavior and innovative green death practices. While scarcely investigated in the marketing and consumer behavior research domains, at least four main reasons spur to consider the funeral industry as an interesting subject of enquiry: i) the sector boasts of a significant economic relevance; ii) it is extremely impactful from an environmental and social point of view; iii) it is witnessing interesting innovative processes in the direction of social and environmental sustainability; iv) consumer movements are rising all over the world asking for more sustainable death practices. Thus, adopting an augmented version of the theory of planned behavior (TPB), which includes individuals’ environmentally responsible consumption, and egoistic and altruistic environmental concern, the study investigates behavioral intention to adopt innovative green death practices (a green funeral) of people belonging to three different generations: X, Y, and Z. A purposive sample of 627 Italian people participated in an online the survey. Collected data were analyzed adopting an ordinal logit model. The results confirm the TPB predictive power also in the field of green funeral behavioral intention. Furthermore, environmentally responsible consumption and environmental concern – even though only in its altruistic component – positively influence behavioral intention, and generation exerts a moderating effect among the examined constructs. Overall, the research attempts to enrich the literature on ethical consumption by exploring the underinvestigated phenomenon of buying behavior of unsought and end-of-life products, that of green funerals, and provide managerial recommendations to funeral service sellers.     

Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.