Voluntary disclosure,greenhouse gas emissions and business performance: Assessing the first decade of reporting

This study explores the empirical relationships between GHG emissions and an extensive range of business performance measures for UK FTSE-350 listed firms over the first decade or so of such reporting. Despite the popular and policy generated environmental imperatives over this period—along with growing evidence of the corporate added-value of having an ‘environmental conscience’, voluntary disclosure of emissions has been slow to adopt by firms. The leading contribution is to present clear evidence of a non-linear relationship, initially increasing with firm performance and then decreasing. An extensive pattern of non-reporting of emissions is also observed over time, and prior literature has introduced questions of endogeneity existing between firm performance and emissions. Steps are taken to ensure confidence/robustness of the results to these concerns. Accordingly, a two-stage (Heckman-type) selection model is used to analyse the emissions-performance nexus conditional upon the firm choosing to report (i.e. treating the choice to report as being endogenously determined with firm performance). From this—in addition to confirming the robustness of the non-linear relationship—it can be observed that the decision to report emissions is not directly influenced by wider social/governance disclosure attitudes of a firm, thus suggesting that firms disassociate environmental responsibility from social responsibility.     

Business Cycles Synchronisation and Symmetries in the Transition to East African Monetary Union

Our paper explores the prospects for the proposed East African Monetary Union (EAMU) by employing rigorous empirical tools to analyse business cycles synchronisation, structural cross-correlations, spectral decomposition and regional clusters to identify different cyclical episodes, periodicities and characterise the economic cycles of East African countries. We find that cyclical movements reflect various idiosyncratic, common, historical and external shocks in the region. Secondly, all countries appear to be structurally correlated with each other except for South Sudan and Burundi. Our results also observe that the contemporaneous co-movements of East African Community (EAC) cycles with those of Kenya and Tanzaniaare procyclical with coincidental path shift, while the same EAC cycles appear to be acyclical with those of Burundi. Additionally, from the spectral decomposition, Kenyan cycles take 10 years to complete, while those of Tanzania and Rwanda take 8 years. Ugandan and Burundian cycles take approximately 5 years, while the cyclical frequency for South Sudan corresponds to 3.3 years. Finally, the cluster characterisation of countries reveals that South Sudan, Burundi and Rwanda form a group, while Kenya and Tanzania from a group distinct from the rest. We urge the member countries to prioritise policies on regional risk-sharing and adjustment mechanisms, in addition to establishing credible institutional infrastructure that ensures surveillance and enforcement of convergence conditions adopted in EAMU protocol.     

Climate,diseases, and the origins of corruption

It has been commonly observed that tropical countries tend to suffer from intense corruption and underdevelopment. This study provides an explanation for this long-standing disparity across the world based on variation in the intensity of ultraviolet radiation (UV-R). The central hypothesis is that UV-R is positively associated with the (historical) prevalence of eye diseases, which significantly shortens work-life expectancy as a skilled worker. This helps shape the worldwide distribution of corruption by affecting the incumbents' window of opportunity. Using data for up to 139 countries, I consistently find empirical support for the positive relationship between UV-R and corruption. The main findings withstand accounting for numerous alternative explanations for international differences in corruption levels. Employing individual-level data from the World Values Survey, I document suggestive evidence that exposure to UV-R is linked to surveyed respondents' tolerance towards corrupt activities. Furthermore, a subnational analysis for China lends credence to the cross-country evidence.     

创业企业何以提高创新绩效——基于创业学习与商业模式创新协同联动视角的QCA方法

以150个创业企业为样本，基于创业学习和商业模式创新理论，运用模糊集定性比较分析方法（fsQCA），探究经验学习、认知学习、新颖型商业模式创新和效率型商业模式创新对创新绩效的影响路径及机制。研究发现，存在新颖型商业模式创新等4条创新绩效产出关键路径；相较于效率型商业模式创新，新颖型商业模式创新在关键路径中更容易提高创新绩效；利用QCA方法证实了影响创业企业创新绩效的组态内部各要素之间的替代性。结论丰富了创新绩效产出内部机制，可为创业企业提高创新绩效提供指导和借鉴。     

MORE THAN A FEELING: CONFIDENCE,UNCERTAINTY, AND MACROECONOMIC FLUCTUATIONS

Economists, observers, and policy-makers often emphasize the role of sentiment as a potential driver of the business cycle. In this paper, we provide three contributions to this debate. First, we give an overview of the recent literature on the nexus between sentiment (considering both confidence and uncertainty) and economic activity. Second, we review existing empirical measures of sentiment, in particular consumer confidence, stock market volatility (SMV) and Economic Policy Uncertainty (EPU), on monthly data for 27 countries, 1985–2016. Third, we identify some new stylized facts based on international evidence. While different measures are surprisingly lowly correlated on average in each country, they are typically highly positively correlated across countries, suggesting the existence of a global factor or sizeable international spillovers of sentiment. Consumer confidence has the closest co-movement with economic and financial variables, and most of the correlations are contemporaneous or forward-looking, consistent with the view that economic sentiment is indeed a driver of activity.     

技术创业型企业孵化资本投入的风险管控——来自多案例对比

待孵化的技术创业型企业同时具有概念性技术向差异化产品转化的高风险与产品异质性被认同后溢价评估的高收益特点，导致其投资者多处于隔岸观火却欲罢不能的的孵化投资矛盾中。对孵化资本实施有效的风险管控，成为吸引外部资本投入的关键。基于此，选择在可创概念选育与技术创业孵化领域探索出高成功率路径的概念证明中心、YCombinator和创新工场为研究案例，分析其在创业项目筛选、孵化资金运营等关键环节的风险管控方式，以期完善我国孵化资本管控理论、助力我国商业孵化器提高资金风险管控能力、促进外部资本投入。     

The digital transformation of business models in the creative industries: A holistic framework and emerging trends

This paper examines how digital technologies facilitate business model innovations in the creative industries. Through a systematic literature review, a holistic business model framework is developed, which is then used to analyse the empirical evidence from the creative industries. The research found that digital technologies have facilitated pervasive changes in business models, and some significant trends have emerged. However, the reconfigured business models are often not ‘new’ in the unprecedented sense. Business model innovations are primarily reflected in using digital technologies to enable the deployment of a wider range of business models than previously available to a firm. A significant emerging trend is the increasing adoption of multiple business models as a portfolio within one firm. This is happening in firms of all sizes, when one firm uses multiple business models to serve different markets segments, sell different products, or engage with multi-sided markets, or to use different business models over time. The holistic business model framework is refined and extended through a recursive learning process, which can serve both as a cognitive instrument for understanding business models and a planning tool for business model innovations. The paper contributes to our understanding of the theory of business models and how digital technologies facilitate business model innovations in the creative industries. Three new themes for future research are highlighted.     

Internet of Things and Big Data as enablers for business digitalization strategies

Digitization blurs the lines between technology and management, facilitating new business models built upon the concepts, methods and tools of the digital environment. The purpose of this study is to investigate the role of the Internet of Things (IoT) and Big Data in terms of how businesses manage their digital transformation. The paper argues that the outbreak of IoT and Big Data has resulted in a mass of disorganized knowledge. In order to make sense of the noise, a literature review was carried out to examine the studies, published in the last decade (2008–2019), that analyzed both the Internet of Things and Big Data. The results show that IoT and Big Data are predominantly reengineering factors for business processes, products and services; however, a lack of widespread knowledge and adoption has led research to evolve into multiple, yet inconsistent paths. The study offers interesting implications for managers and marketers, highlighting how the digital transformation enabled by IoT and Big Data can positively impact many facets of business. By treating IoT and Big Data as faces of the same coin, this study also sheds light on current challenges and opportunities, with the hope of informing future research and practice.     

Stabilizing and destabilizing mechanisms: A new perspective to understand business cycles

Economists believe that economic fluctuations can be smoothed by stabilization mechanisms, such as price adjustment, embedded in the economy. While price adjustment can be seen as a stabilization mechanism, are there mechanisms that can destabilize an economy? We find that as early as 1939, Harrod discussed a destabilization mechanism, the firm's investment adjustment, illustrated in his knife-edge puzzle. We build a macro-dynamic model with investment and price as the core macroeconomic variables. Our analysis shows that the interaction between the stabilization mechanism (price adjustment) and the destabilization mechanism (investment adjustment) generates fluctuations and cycles. However, due to price stickiness, the price adjustment mechanism may not be enough to stabilize the economy. In this case, a government stabilization policy is necessary for further stabilization. As this paper also addresses the microfoundations of Keynesian quantity theory, including the choice of output and investment in optimization, it can be related to traditional Keynesian economics, with a new perspective to understand business cycles.     

Stock market reactions to domestic sentiment: Panel CS-ARDL evidence

This study sets out to explore the effects of business and consumer sentiment on stock market performance, within the separate contexts of advanced and emerging markets. The empirical analysis is carried out using the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) modeling approach, which considers time dynamics, cross-sectional heterogeneity, and cross-sectional dependence. The findings for developed markets suggest that business sentiment has positive leading effects on stock returns, across short- and long-term time horizons, while for emerging markets, the price impact of business sentiment turns out to be short-lived. On the other hand, consumer sentiment tends to affect positively both market types, albeit only in the short run. Furthermore, the influence of sentiment indicators seems to be stronger in emerging- than in developed-market countries. The results remain robust, even after controlling for a rich range of potential predictors of stock returns. Generally, such evidence highlights the relevance of psychological factors, such as business and consumer sentiment, in determining the future trajectory of asset prices.