The Dynamics behind the Likelihood of Adopting Inclusive Agrarian Innovations in Disadvantaged Central American Communities

Although innovation studies form a consolidated field in developed countries, the same is not true in disadvantaged countries especially in agriculture, despite the importance of innovation in generating wealth and inclusiveness. With this study, we aim to contribute to the knowledge of the processes of adopting innovation in agrarian contexts of poverty. Thus, we examined the main factors that influence the probability of accepting a new product, and their interrelationships in a Central American community. Based on a qualitative methodology, we held 42 in-depth interviews with small-scale producers. All the information collected was the subject of a discursive and content analysis, with support from the NVivo 12 software programme. The results show how key factors such as culture, the market, networks, attitudes, expectations, and social references are interrelated and enhanced or hindered by other social dynamics. These findings underline the idea that the entrepreneur’s relationship with innovation is a dynamic reality where the probability of acceptance is the outcome of combining cultural, individual, institutional, and organisational factors. Any innovation support policy that arises in these contexts should be based on more systemic approaches if the acceptance of inclusive innovation is to be improved.

PENGARUH KEMAMPUAN MANAJEMEN DAN POLA PENDAMPINGAN TERHADAP PENGEMBANGAN USAHA MELALUI JEJARING PASAR (STUDI PADA USAHA KECIL DAN MENENGAH DI KABUPATEN BALANGAN)

This study aims to determine both partially and simultaneously how much influence the management ability and mentoring pattern have on business development through market networks (Studies on Small and Medium Enterprises in Balangan Regency) and the variables that influence both directly and indirectly on market development.The method used in this study is a quantitative method and with a population of 5,124 (five thousand one hundred and twenty-four) UMKM, samples were taken using a proportionate stratified random sampling technique using the Slovin formula as many as 98 UMKM, the research instrument was validity and reliability test, the data were tested using linear regression Path analysis using Smart PLS.The results of this study indicate that there is a direct influence of Management Ability, Assistance Pattern and Market Networking influence on Business Development and indirect influence on Management Ability, on Business Development through market networks and does not affect Development Pattern on Business Development through market networks..Keywords: management ability, mentoring pattern, market network and business development. Abstrak: Penelitian ini bertujuan untuk mengetahui baik secara parsial dan simultan seberapa besar Pengaruh Kemampuan Manajemen Dan Pola Pendampingan Terhadap Pengembangan Usaha Melalui Jejaring Pasar (Studi Pada Usaha Kecil Dan Menengah Di Kabupaten Balangan) serta variabel yang berpengaruh Baik itu secara langsung dan tidak langsung terhadap pengembangan pasar. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif dan dengan populasi sebanyak 5.124 (lima Ribu Seratus Dua Puluh Empat) UMKM, diambil sampel dengan teknik proportionate stratified random sampling dengan menggunakan rumus Slovin sebanyak 98 UMKM, instrument penelitian uji validitas dan uji reliabilitas, data – data diuji dengan menggunakan regresi linear Analisis Jalur menggunakan Smart PLS. Hasil penelitian ini menunjukkan bahwa terdapat pengaruh secara langsung Kemampuan Manajemen, Pola Pendampingan dan Jejaring Pasar berpengaruh terhadap Pengembangan Usaha dan tidak langsung Kemampuan Manajemen, terhadap Pengembangan Usaha melalui jejaring pasar dan tidak berpengaruh Pola Pengembangan terhadap Pengembangan Usaha melalui jejaring pasar. Kata kunci : kemampuan manajemen, pola pendampingan, jejaring pasar dan pengembangan usaha.

Loss of structural balance in stock markets

We use rank correlations as distance functions to establish the interconnectivity between stock returns, building weighted signed networks for the stocks of seven European countries, the US and Japan. We establish the theoretical relationship between the level of balance in a network and stock predictability, studying its evolution from 2005 to the third quarter of 2020. We find a clear balance–unbalance transition for six of the nine countries, following the August 2011 Black Monday in the US, when the Economic Policy Uncertainty index for this country reached its highest monthly level before the COVID-19 crisis. This sudden loss of balance is mainly caused by a reorganization of the market networks triggered by a group of low capitalization stocks belonging to the non-financial sector. After the transition, the stocks of companies in these groups become all negatively correlated between them and with most of the rest of the stocks in the market. The implied change in the network topology is directly related to a decrease in stock predictability, a finding with novel important implications for asset allocation and portfolio hedging strategies.

Model selection in stock correlation networks

In this research we construct market networks to study correlation between the price return for all Dow Jones, NASDAQ-100 and S&P 100 indices that were traded over a period of time. We consider market networks, which have stocks as nodes and edges corresponding to correlated stocks. Specifically, a winner-take-all approach is used to determine if two nodes are adjacent. We identify that all networks based on the connecting stocks of highly correlated price returns display a scale-free degree distribution. Additionally, we use features for representing different aspects of the network. The feature includes small connected sub-graphs with three and four vertices. We use an algorithm to count frequently the number of the graphlets for our mathematical models and our constructed networks. Each network is assigned an 8-dimensional vector. We present a model selection algorithm based on supervised learning. Our algorithm classifies our market networks with the best fitting mathematical model.

Model selection in stock correlation networks

In this research we construct market networks to study correlation between the price return for all Dow Jones, NASDAQ-100 and S&P 100 indices that were traded over a period of time. We consider market networks, which have stocks as nodes and edges corresponding to correlated stocks. Specifically, a winner-take-all approach is used to determine if two nodes are adjacent. We identify that all networks based on the connecting stocks of highly correlated price returns display a scale-free degree distribution. Additionally, we use features for representing different aspects of the network. The feature includes small connected sub-graphs with three and four vertices. We use an algorithm to count frequently the number of the graphlets for our mathematical models and our constructed networks. Each network is assigned an 8-dimensional vector. We present a model selection algorithm based on supervised learning. Our algorithm classifies our market networks with the best fitting mathematical model.

The Role of Institutions in Creating Circular Economy Pathways for Regional Development

In the past decade, the circular economy has gained attention as a mechanism of transition toward a regenerative, low carbon, and resource-efficient society. As the history of previous radical transformations shows, successful transition toward the circular economy cannot take place without understanding the institutional features of industrial transformations. This article highlights the significance of institutions by placing the circular economy model in the context of the natural resource–based sector and discusses the importance of institutions in regional path development. The article identifies three institutional determinants of both endogenous and directed transformation toward the circular economy model in the regional context: (i) proximity of physical flows and assets, (ii) maturation and diversity of market networks, and (iii) inherent values and patterns of cooperation. This article offers a starting point for future studies of circular economy transitions and the role of institutions as enabling, as well as at times obstructing transition environments.

Housing market networks in China's major cities: a conditional causality approach

PurposeTo explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.Design/methodology/approachAlthough housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.FindingsWe discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.Originality/valueBy excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

Measuring competitiveness through the global competitiveness project framework: the Brazilian experience

Purpose The purpose of this paper is twofold: first, to validate the global competitiveness project (GCP) framework in the Brazilian context; second, to describe the competitiveness levels on a sample of Brazilian firms, searching for heterogeneities of size, age and industry. Design/methodology/approach The study used the theoretical-empirical GCP framework, comprising the dimensions: human capital, product, domestic market, networks, technology, decision-making, competitive strategy, marketing, internationalization and online presence (Lafuente, Szerb and Rideg, 2016; Lafuente et al., 2019) and applied descriptive statistics, correlation analysis, confirmatory factor analysis and cluster analysis, on a survey data set of 55 Brazilian firms from different sizes, ages and industries. Findings The GCP framework was found robust, reliable and useful in emerging economies as the Brazilian. Three clusters of competitiveness were identified. Heterogeneities were detected in knowledge-intensive business services results. This work allows a better understanding of competitiveness through the identification and measurement of dimensions, which can help managers to identify/audit capacities to plan/improve firm performance. Practical implications Findings may support managers to identify, estimate and manage their competitiveness pillars, and thus increase their competitiveness levels with a focus on strategic long-term goals. Originality/value This paper contributes to knowledge production in two ways: to the validation of the framework in the Brazilian scenario and the understanding of the dynamics of competitiveness of firms.