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The chapter advances some critical reflections around commons and commoning in the smart city. It suggests that so-called smart commons – that is, forms of ownership of data and digital infrastructure increasingly central to the discourse around appropriation and co-production of smart technologies – tends to focus more on the outcome (open data or free software) rather than the process which maintains and reproduces such commons. Thus, the chapter makes a positional argument for a “smart approach” to the commons, advocating for a central role for the public as a stakeholder in advancing, nurturing, and maintaining urban commons in the smart city. The argument is illustrated through three brief case studies which reflect on instances of commons and commoning in relation to the implementation of public Internet infrastructure.

School leaders in small to mid-size urban districts face shifting policy environments, increased accountability, fiscal austerity, and unfunded mandates, as they work to improve student learning and close achievement gaps. This chapter focuses on one aspect of school reform: the role of families in supporting students’ success. Given shifting demographics nationwide, recommendations for two-way partnerships with Latino families will be proffered in light of renewed definitions and an increasingly robust research base.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis periods. We compute the measures for the volatility of liquidity by using bid-ask spread data sampled at a high frequency of five minutes. By examining 11 currencies over a 13-year sample period, we utilize a balanced dynamic panel regression to investigate whether the risk associated with the currencies quoted or trading activity affects the variability of liquidity provision in the FX market and examine whether the crisis periods have any effect. We find that both the level of spread and volatility of spread increases during the crisis periods for the currencies of emerging countries. In addition, we find increases in risks associated with the currencies proxied by realized volatility during the crisis periods. We also show risks associated with the currency are the major determinants of the variability of liquidity and that these relationships strengthen during periods of uncertainty. First, we develop measures to capture the variability of liquidity. Our measures to capture the variability of liquidity are non-parametric and model-free variable. Second, we contribute to the debate of whether variability of liquidity is adverse to market participants by examining what drives the variability of liquidity. Finally, we analyze seven crisis periods, allowing us to document the effect of the crises on determinants of variability of liquidity over time.

Implementing the sustainable development goals (SDGs) has proven a significant challenge for companies. While multinational enterprises (MNEs) have shown a real intention to contribute to these goals, they face major barriers in implementing the SDGs in their core business strategies. Extant academic studies on this phenomenon have primarily explored why companies “should” address the SDG agenda but have not (yet) explored what “works,” what does not “work,” and why. Therefore, evidence of a sizable gap between intention and realization is growing. Besides, there is a limited explanation for the existence of this gap and no validated implementation models that could help overcome it. Additionally, management research remains relatively fragmented. The diversity of existing theoretical and empirical frameworks makes it difficult to consolidate scientific and practical insights on “how” to guide companies to accelerate the global goals through their core operations.

This study is one of the first attempts to draw lessons from extant research on effective SDGs’ implementation strategies. For that, we upgrade the “SDG Compass,” which has been recognized as a leading framework for SDGs implementation in companies’ core activities. A critical assessment of the literature on the SDGs implementation has been conducted through a systematic literature review (SLR) and bibliometric analysis. This has helped us identify gaps in the SDG implementation practice and accumulate relevant insights supporting a more integrated and upgraded implementation framework: the SDG Compass+. This framework can advance coordinated theoretical and practical research by identifying the antecedents and critical factors of impactful SDG implementation strategies.

Distributed performance arrangements are increasingly used by organizations to structure dyadic and team interactions. Unfortunately, distributed teams are no panacea. This chapter reviews some of the advantages and disadvantages associated with the geographical and temporal distribution of team members. An extended discussion of the implications of distributed team performance for individual, team, and organizational decision making is provided, with particular attention paid to selected cultural factors. Best practices and key points are advanced for those stakeholders charged with offsetting the performance decrements in decision making that can result from distribution and culture.