农业创业情境下商业模式设计的影响机制研究——基于模糊集定性比较分析

农业创业者如何通过整合与管理资源与能力，设计新颖型商业模式创造更多价值，是实现乡村振兴战略的关键措施。文章基于资源编排、商业模式理论，以央视《致富经》栏目40个案例为样本，运用模糊集定性比较分析（fsQCA）方法，剖析资源编排、机会能力、创业学习所构成的前因变量组态对农业创业活动中商业模式设计的影响机制。研究发现，资源编排在农业创业活动中发挥不可或缺的关键作用；相较于资源结构化和资源捆绑，资源利用在新颖型商业模式设计中的作用更为突出；机会能力须与资源编排共同作用，方可实现新颖型商业模式设计；在资源编排作用缺乏时，创业学习的发挥有助于新颖型商业模式设计的形成。研究成果诠释了农业创业情境下资源编排、机会能力与创业学习对商业模式设计的影响机制，为开展农业创业活动提供指导。     

Business model innovation in cultural and creative industries: Insights from three leading mobile gaming firms

This paper contains an exploratory analysis of the business model innovations (BMIs) that firms in cultural and creative industries (CCIs) undertake along their life-cycle. Despite the role that creative and cultural and creative firms (CCFs) have in the economic development of industrialised countries, they tend to remain small and often fail due to industry-specific constraints and tensions, such as the lack of managerial capabilities and complexity in nurturing value chain relationships. However, there has been relatively limited scholarly interest into the specific conditions and processes that enabled CCFs to overcome these liabilities, and in particular into the identification of the business models they have adopted along their life-cycle. In this paper, this issue is analysed by using the concept of BMI, which sheds light on how the reconfiguration of the activity system through which a CCF creates, delivers and captures value enables the exploitation of entrepreneurial opportunities over time. This paper builds on an in-depth historical study of three leading firms operating in the mobile gaming industry, namely Rovio, Zynga and King Digital Entertainment. Three main results emerge from this study. First, in the ramp-up phase of their life-cycle, CCFs organize their resource architecture to build a strong and recognized reputation. Second, in the development phase, BMI is used to leverage new distribution paradigms. Finally, in the maturity phase, firms dedicate resources to innovate their product portfolios by providing platforms that support the development and testing of new creative ideas and solutions. Findings and implications are then discussed.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

The impact of the COVID-19 pandemic on business expectations

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms report significant or severe disruptions to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruptions, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively impacted by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms’ one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms’ near-term inflation expectations, their longer-run inflation expectations have remained relatively stable.     

FX Intervention in the New Keynesian Model

We develop an open-economy New Keynesian Model with foreign exchange (FX) intervention in the presence of a financial accelerator and shocks to risk appetite in international capital markets. We obtain closed-form solutions for optimal monetary and FX intervention policies assuming the central bank cannot commit to future policies, and we compare the solution to that under policy commitment. We show how FX intervention can help reduce the volatility of the exchange rate, of inflation, and of the output gap, thus mitigating welfare losses associated with shocks in the international capital markets. We also show that, when the financial accelerator is strong, there is a risk of indeterminacy (self-fulfilling currency and inflation movements) although FX intervention can reduce this risk and thus reinforce the credibility of the inflation targeting regime. Model simulations match well the impact of a VIX shock obtained by local projections on a panel of inflation targeting emerging markets.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Paired comparison models with age effects modeled as piecewise quadratic splines

We propose new models for analyzing pairwise comparison data, such as that relating to sports. We focus on changes in players’ strengths and the prediction of future results. Our models are based on the Thurstone-Mosteller and Bradley–Terry models, and make use of the time variation in the parameters. Furthermore, we apply our models to data from the Japanese traditional sport sumo, and analyze this data. The proposed models perform better than the standard Thurstone-Mosteller and Bradley–Terry models according to both the Akaike information criterion and the Brier score. We compare the proposed models in detail by focusing on individual sumo wrestlers.     

Politically connected boards,family and business group affiliations,and cost of capital: Evidence from Indonesia

We investigate the effect of politically connected boards (both supervisory boards [SBs] and boards of directors [BODs]) on cost of debt and equity capital of listed companies in Indonesia which has established a two-tier corporate governance system. The results, based on 250 firms, suggest that companies with politically connected SBs experience lower cost of debt and equity capital, whereas politically connected BODs have no association with cost of either debt or equity. Furthermore, we find that family firms and firms belonging to business groups with politically connected SBs enjoy lower cost of debt and equity capital. Our main results are robust to alternative measures and to tests for endogeneity.     

US small retail businesses’ perception of competition: Looking through a lens of fear,confidence, or cooperation

The U.S. retail industry has undergone dramatic changes, leading to the closing of brick-and-mortar retail stores on a large scale. Understanding perceived market competition among small retail businesses may help explain why certain businesses survive or fail in the altered retail environment. This study investigates small retail businesses' perceptions of competition within/outside the community, underlying reasons for their perceptions, and variations in perceptions by different business characteristics. Contrary to common expectations, we found that more than half of businesses interviewed were optimistic in the face of competition. Our data reveal two salient underlying reasons for pessimism and five for optimism. These perceptions tended to differ by operational locality (urban clusters vs. urban areas), business revenues, and innovativeness. Based on the findings, we developed propositions and a framework of small businesses' perceived competition and sustainable competitive advantage. We discussed theoretical and practical implications for small retail businesses’ sustainable growth in the challenging retail environment.     

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.