Exogenous Attributes Research Articles

Digital Development Influencing Mechanism on Green Innovation Performance: A Perspective of Green Innovation Network

Digital development offers groundbreaking opportunities for environmental monitoring and protection, as well as for maintaining the overall health of the planet. Through judicious use, global sustainability can be promoted. This study constructs a green innovation network using the green innovation patent data of listed companies of new energy vehicles as empirical evidence. The influence of network structure on green innovation performance is analyzed, and network structure indicators with positive effects are identified. The Exponential Random Graph Model (ERGM) is used to analyze the evolutionary influence on the structure of green innovation network from network configuration as endogenous attributes and digital characteristics as exogenous attributes. We explore the mechanism of improving green innovation performance by digitalization through enhancing the development of green innovation network. The results show that the enhanced structure of innovation network has a significant positive effect on the improvement of green innovation performance. Both "star" and "closed triangle" network configurations can promote the establishment of green innovation cooperation. In terms of digital characteristics, digital technology and digital organization can positively promote the development of green innovation network, while digital environment does not show a significant facilitating effect. Because of the tremendous expansion of digital technology adoption and the necessary infrastructure, enterprises can bridge the technology gap by optimizing digital technology and digital organizations. Enterprises can improve green innovation performance by optimizing digital technology and digital organization to promote the establishment of green innovation cooperation. The government should avoid the ineffective empowerment of digital environment construction on green innovation performance.

The impact of the Belt and Road Initiative on the natural gas trade: A network structure dependence perspective

Given the increasing complexity of multilateral relations in the natural gas trade along the Belt and Road (B&R), it is necessary to quantify the impact of the Belt and Road Initiative (BRI) on natural gas trade from the perspective of structure dependence. First, the multilateral trade of natural gas along B&R is abstracted as the liquefied natural gas and pipeline natural gas trade networks (BR-LNGTN and BR-PNGTN, respectively). Second, we analyze the evolution of the network structure and the status change of countries before and after the implementation of the BRI. Finally, based on the temporal exponential random graph models (TERGMs), the evolutionary mechanism of the natural gas trade network and impact of the BRI on the evolutionary mechanism are comprehensively studied. The results show that the implementation of the BRI promotes the network size, but the advantages of trade cooperation have not been fully realized. China and India are the authoritative trading countries, while Russia and Qatar are the primary intermediary countries. Structure dependence, time dependence and trade facilitation significantly impact the generation of the network, but the explanatory power of the exogenous attributes decrease, which indicates that the trade conditions along B&R have been significantly improved.

Identification and inference of network formation games with misclassified links

This paper considers a network formation model when links are potentially misclassified. We focus on a game-theoretical model of strategic network formation with incomplete information, in which the linking decisions depend on agents’ exogenous attributes and endogenous network characteristics. In the presence of link misclassification, we derive moment conditions that characterize the identified set for the preference parameters associated with homophily and network externalities. Based on the moment equality conditions, we provide an inference method that is asymptotically valid when a single network of many agents is observed. Finally, we apply our misclassification-robust method to study the preference parameters of a lending network in rural villages in southern India.

Modeling venture capital networks in hospitality and tourism entrepreneurial equity financing: An exponential random graph models approach

This study examines how venture capital (VC) networks form when financing hospitality and tourism start-ups. We propose that networks form as a result of exogenous attributes along with endogenous dependencies, which have been largely under-studied in the hospitality and tourism finance literature. Using a purposefully developed dataset of 644 VC investment deals, this study employed exponential random graph models (ERGMs) to examine how homophily in VC investor-specific attributes influences the tie formation, and to model the likelihood of future tie formation in VC networks in terms of endogenous dependencies. Results reveal that homophily among VC investors increases the likelihood of tie formation, while the establishment of future ties shows a significant tendency of multiple triangulation and connectivity in VC networks. The study provides insights into entrepreneurial equity financing from the VC perspective. It advances the network research and methodology by revealing the underlying structural processes of network formation through ERGMs. • Examining venture capital (VC) investment in hospitality and tourism entrepreneurial finance. • Modeling endogenous dependencies along with exogenous attributes in VC networks. • Advancing network research and methodology with the application of exponential random graph models (ERGMs). • Homophily among VC investors increases the likelihood of tie formation. • The establishment of future ties shows significant tendency of multiple triangulation and connectivity in VC networks.

The relative choice over destiny in a country’s long-run economic growth and economic affluence

This paper examines an in-depth and systematic review of why some nations are so rich, while others remain so poor taking into account temporal and spatial dynamics applied for economic growth covariants. Growth literature underscores direct and indirect causes for economic growth. Likewise, economic and non-economic dynamics are thoroughly examined for countries’ long-run economic growth and relative wealth accumulation. Endogenous growth theories emphasized that investment in human capital, innovation, and knowledge are major contributors to economic growth. Empirics confirmed that time-variant (such as well-established institutions and their prominent role in devising property rights and policies) gives more emphasis towards shaping a country’s long-run economic growth than time-invariant exogenous attributes (like geography). Meanwhile, some nations did not industrialize being geographically advantageous, and the location of the country does not exclusively determine the fate of the nation’s economic success. Moreover, state capacity is vital to determining relative wealth accumulation and economic prosperity. The incidence of routine war undermines the fiscal capacity and leads to an extractive form of government and weakens public and private investments, and this sets a country into undesirable outcomes. In a nutshell, time-varying attributes become more flexible to adjust the fate of countries’ economic growth and destiny. Furthermore, it requires an intense investigation of what governs a nation’s economic successes or failures focusing on country-specific concerns. It needs a close and continuous rectification to reconsider the country’s institutional setup and policy frameworks towards endogenously rooted economic growth and development for the relative economic affluence.

Australian Consumers' Preferences for Food Attributes: A Latent Profile Analysis.

Understanding consumer food preferences can provide agribusinesses with a competitive advantage through meeting consumers’ needs. Consumers’ preferences for food attributes have been extensively examined, focusing on specific aspects of attributes with specific food products. It is less clear how consumers evaluate the relative importance of the key food attributes in general. Applying the commonly adopted classification of food attributes into endogenous attributes (i.e., safety and freshness) and exogenous attributes (i.e., genetically modified (GM)-free and organic), the relative importance of these attributes for consumers was investigated. Furthermore, the heterogeneity of preferences was explored to identify distinct subgroups of consumers who may differ in valuing various food attributes. An online survey of 489 city dwellers in Australia revealed that the endogenous attributes were regarded as the most important in an order of safety and freshness. The exogenous attributes were rated as much less important. Three profiles with distinctive preferences for food attributes were identified: Not Fussy (12% of participants), Quality First (49%) and Choosy (39%). The findings suggest that consumers value the importance of various food attributes in a hierarchical order, and there is significant heterogeneity in consumers’ food preference. The implications of these findings are discussed in the context of food policy and agribusiness decision-making.

Child Skill Accumulation in One- and Two-Parent Families

In this paper, I examine the role of family structure in child skill accumulation. I establish empirically that one- and two-parent families use different technologies to invest in their children: the choices of one-parent families are more sensitive to child care’s price. I analyze the effect of child care subsidies using a general equilibrium framework with endogenous family formation. I find that subsidies improve skill outcomes for children, especially those raised by one-parent, low-income families, thereby narrowing the gap in child outcomes across family structures. Exogenous attributes of family structure drive between 28 and 40 percent of this gap.

The emergence of properties of the Japanese production network: How do listed firms choose their partners?

This paper investigates the mechanisms behind the formation of links between suppliers and customers in the Tokyo Stock Exchange (TSE) production network. The supplier-customer network of listed firms in Japan is estimated by the so-called exponential random graph model (ERGM). We estimate 4 models to understand how endogenous and exogenous attributes contribute to the emergence of the TSE production network. In terms of endogenous attributes, the probability of links between suppliers and customers increases with the transitivity and reciprocity. We show that the model fits the real data only when the geographic distance, firm sizes the industrial sector of the firms are considered.

Tie-formation process within the communities of the Japanese production network: application of an exponential random graph model

This paper studies the driving forces behind the formation of ties within the major communities in the Japanese nationwide network of production, which contains one million firms and five million links between suppliers (“upstream" firms) and customers (“downstream" firms). We apply the Infomap algorithm to reveal the hierarchical structure of the production network. At the second level of the hierarchy, we find a reasonable community resolution, where the community size distribution follows a power law decay. Then, we estimate the tie formation within 100 communities of different sizes. The studied model considers a large set of attributes, including both endogenous attributes (network motifs, e.g., stars and triangles) and exogenous attributes (economic variables, e.g., net sales and firm size). The estimation results show that the considered model converges and presents a high goodness of fit (GoF) for all communities. Moreover, it is found that the forces explaining the formation of links between suppliers and customers differ among communities. Some attributes, such as reciprocity, popularity, activity, location homophily, bank homophily and sales statistics, are common drivers of internal link formation for most of the studied communities. However, transitivity is rejected as a significant influencing factor for most communities, reflecting an absence of a sense of trust and reliability between firms with a common partner. Finally, we show that sector homophily does not serve as an obvious mechanism of partnership at the community level in the production network.

Homophily and the Persistence of Disagreement

We study a dynamic model of attitude formation in which individuals average others' attitudes to develop their own. We assume that individuals exhibit homophily in sociodemographic exogenous attributes, that is, the attention they pay to each other is based on whether they possess similar attributes. We also assume that individuals exhibit homophily in attitudes, at the group level. Specifically, attributes that are salient, that is, that exhibit a substantial difference in attitudes between the groups of individuals possessing and lacking them, deserve high attention. Since we allow attention to evolve over time we prove that when there is, initially, a unique most salient attribute, it deserves growing attention overtime in detriment of the remaining ones. As a result, individuals eventually interact only with others similar to them across this attribute and disagreement persists. It materializes in two groups of thinking defined according to this attribute.

Homophily and prestige: An assessment of their relative strength to explain link formation in the online climate change debate

Previous work has shown that hyperlinks reflect actors’ strategic choices; these dyadic relationships depend on the actors’ exogenous attributes (e.g., homophily) and the network’s endogenous features (e.g., prestige distribution among actors). We combine these factors as explanatory variables in different exponential random graph models (ERGMs) to assess the relative strength of prestige and homophily for the actors’ link formation. We analyze the climate change discourse in a hyperlink network formed by US civil society actors from November 2014 and test how relevant the different factors are, including variables such as actor type, country, position, and topic. We find that both prestige and various aspects of homophily influence link formation online. With regard to the importance of the different factors, positional homophily stands out, followed by prestige and other homophily effects.

커피산업에서의 서비스가 감정 및 만족에 미치는 영향

This research investigates the effect of service on the emotion and satisfaction in the domain of coffee business. This study examined the accessibility, comfort, price fairness, and mobile app convenience as exogenous attributes to account for the emotion and satisfaction. The research context is Starbucks. For the data collection, this research adopted Amazon Mechanical Turk using survey. This research employed structural equation model for the data analysis. It includes confirmatory factor analysis, path analysis, and correlation analysis. The results indicated that proposed attributes are significantly associated with the emotion and satisfaction. This study also found that emotion positively impacted on the customer satisfaction in the coffee service domain.

Understanding the Effects of Various Factors on the Public Response to Congestion Charge: A Latent Class Modeling Approach

The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an innovative latent class based generalized ordered response model (LC-GORM) is formulated and used to assess the effects of various factors on respondents’ choice behavior with respect to congestion charge proposal for Jakarta, Indonesia. The proposed model probabilistically assigns respondents into selfish and altruistic class memberships (latently) based on their knowledge of the proposed scheme and their specific attributes. Aiming to capture observable preference heterogeneity across ordinal choices and allow the thresholds to be varied across observations, we parameterize the thresholds as a linear function of the exogenous variables for each ordinal preference. Using stated preference data collected in Jakarta in December 2013, we incorporate the influence of a comprehensive set of explanatory variables into four categories: charges, latent variables related to respondent’s psychological motivations, mobility attributes and socio-demographic characteristics. Empirical results obviously verify the existence of preference heterogeneity across outcomes. The findings confirm that the altruistic class are more sensitive with respect to acceptance of the scheme, while the selfish class are more sensitive with respect to rejection. The key factors influencing public acceptability include the charge level and respondent variables such as car dependency, awareness of the problem of cars in society, frequency of visits to the city center and frequency of private mode usage.

Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control

Operators currently controlling unmanned aerial vehicles report significant boredom, and such systems will likely become more automated in the future. Similar problems are found in process control, commercial aviation and medical settings. To examine the effect of boredom in such settings, a long-duration low-task-load experiment was conducted. Three low-task-load levels requiring operator input every 10, 20 or 30 min were tested in a 4-h study, using a multiple unmanned vehicle simulation environment that leverages decentralized algorithms for sometimes-imperfect vehicle scheduling. Reaction times to system-generated events generally decreased across the 4 h, as did participants' ability to maintain directed attention. Overall, the participants spent almost half of the time in a distracted state. The top performer spent the majority of time in directed and divided attention states. Unexpectedly, the second-best participant, only 1% worse than the top performer, was distracted for almost one-third of the experiment, but exhibited a periodic switching strategy, allowing himself to pay just enough attention to assist the automation when needed. Indeed, four of the five top performers were distracted for more than one-third of the time. These findings suggest that distraction due to boring, low-task-load environments can be effectively managed through efficient attention switching. Future work is needed to determine optimal frequency and duration of attention state switches, given various exogenous attributes, as well as individual variability. These findings have implications for the design of and personnel selection for supervisory control systems where operators monitor highly automated systems for long durations with only occasional or rare input.

Addressing onsite sampling in recreation site choice models

Independent experts and politicians have criticized statistical analyses of recreation behavior, which rely upon onsite samples due to their potential for biased inference. The use of onsite sampling usually reflects data or budgetary constraints, but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling individuals—a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, exogenous attributes of the individuals sampled onsite may differ from the attributes of individuals in the population—the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two the existing methods: Weighted Exogenous Stratification Maximum Likelihood estimation and propensity score estimation. We use the National Marine Fisheries Service's Marine Recreational Fishing Statistics Survey to illustrate methods of bias reduction, employing both simulated and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers' willingness to pay for improvements in fishing catch, but weighted models exhibit higher variance of parameter estimates and willingness to pay.

Addressing Onsite Sampling in Recreation Site Choice Models

Independent experts and politicians have criticized statistical analyses of recreation behavior that rely upon onsite samples due to their potential for biased inference, prompting some to suggest support for these efforts should be curtailed. The use of onsite sampling usually reflects data or budgetary constraints but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling anglers – a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, the exogenous attributes of the recreational users sampled onsite may differ from the attributes of users in the population – the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two existing methods, Weighted Exogenous Stratification Maximum Likelihood Estimation (WESMLE) and propensity score estimation. We use the National Marine Fisheries Service’s (NMFS) Marine Recreational Fishing Statistics Survey (MRFSS) to illustrate methods of bias reduction employing both simulation and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers’ willingness to pay for additional fishing catch.

A Model of Attention and Anticipation

We develop a model in which people experience standard consumption utility, as well as anticipatory utility defined as the weighted sum of independently anticipated consumption “episodes” or “dimensions”. The weights on these dimensions correspond to the attention that the person pays to the dimension. We assume attention on a dimension increases when expected consumption utility in the dimension differs from expected consumption utility under the default action or the prior belief. We show that the decision maker will pay more for information about dimensions with high expected consumption utility, and the willingness to pay may be negative when expected consumption utility is low. Additionally, when expected consumption utility is sufficiently low, but not when it is high, the decision maker will follow the default action even if it is suboptimal from a consumption standpoint. Furthermore, given the decision maker’s current beliefs and preferences in a dimension, he will consume more in that dimension if he just received information. We then consider an advertisement application in which a monopolist decides whether to certifiably reveal the quality of various exogenous attributes of a good to a consumer who may choose to buy or not. There exists a sequential equilibrium for which the monopolist will not disclose information for attributes in which the consumer’s utility with the highest quality good is sufficiently worse than not buying the good. Competition increases disclosure.

Advances in exponential random graph ( p*) models applied to a large social network

Recent advances in statistical network analysis based on the family of exponential random graph (ERG) models have greatly improved our ability to conduct inference on dependence in large social networks [Snijders, T.A.B., 2002. Markov Chain Monte Carlo Estimation of Exponential Random Graph Models. Journal of Social Structure 3, 2; Pattison, P.E., Robins, G.L., 2002. Neighbourhood-based models for social networks. Sociological Methodology 32, 301–337; Handcock, M.S., 2002. Statistical models for social networks: degeneracy and inference. In: Breiger, R., Carley, K., Pattison, P. (Eds.), Dynamic Social Network Modeling and Analysis. National Academies Press, Washington, DC, pp. 229–240; Handcock, M.S., 2003. Assessing Degeneracy in Statistical Models of Social Networks. Center for Statistics and the Social Sciences, University of Washington, Working Paper No. 39; Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., in press. New specifications for exponential random graph models. Sociological Methodology; Hunter, D.R., Goodreau, S.M. Handcock, M.S., 2005. Goodness of Fit of Social Network Models. Center for Statistics and the Social Sciences, University of Washington, Working Paper No. 47; Goodreau, S.M., Hunter, D.R., Morris, M., 2005. Statistical Modeling of Social Networks: Practical Advances and Results. Center for Studies in Demography and Ecology, University of Washington, Working Paper No. 05-01, previous papers this issue]. This paper applies advances in both model parameterizations and computational algorithms to an examination of the structure observed in an adolescent friendship network of 1681 actors from the National Longitudinal Study of Adolescent Health (AddHealth). ERG models of social network structure are fit using the R package statnet, and their adequacy assessed through comparison of model predictions with the observed data for higher-order network statistics. For this friendship network, the commonly used model of Markov dependence leads to the problems of degeneracy discussed by Handcock [Handcock, M.S., 2002. Statistical models for social networks: degeneracy and inference. In: Breiger, R., Carley, K., Pattison, P. (Eds.), Dynamic Social Network Modeling and Analysis. National Academic Press, Washington, DC, pp. 229–240; Handcock, M.S., 2003. Assessing Degeneracy in Statistical Models of Social Networks. Center for Statistics and the Social Sciences, University of Washington, Working Paper No. 39]. On the other hand, model parameterizations introduced by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., in press. New specifications for exponential random graph models. Sociological Methodology] and Hunter and Handcock [Hunter, D.R., Handcock, M.S., in press. Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics] avoid degeneracy and provide reasonable fit to the data. Degree-only models did a poor job of capturing observed network structure; those that did best included terms both for heterogeneous mixing on exogenous attributes (grade and self-reported race) as well as endogenous clustering. Networks simulated from this model were largely consistent with the observed network on multiple higher-order network statistics, including the number of triangles, the size of the largest component, the overall reachability, the distribution of geodesic distances, the degree distribution, and the shared partner distribution. The ability to fit such models to large datasets and to make inference about the underling processes generating the network represents a major advance in the field of statistical network analysis.

Research area selection in academic departments

The paper treats the topic of long range planning in an applied research oriented university unit. The advantages and disadvantages of the relevance of planning in an academic frame are considered, and the need for a formal long‐term consideration of the development of an academic research unit is presented. Endogenous and exogenous attributes of future desirable fields of research are determined. The desired attributes are translated into selection criteria and quantitative indicators. A compensatory type methodology for decision‐making is applied in order to rank potential areas of research. An example of the application of the proposed methodology for selecting directions of development for an applied chemistry academic unit (Casali Institute of Applied Chemistry, The Hebrew University of Jerusalem) is presented and the results are discussed.