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The study aims to identify the gaps and the potentialities of citizen-generated data in four axes of warning system: (1) risk knowledge, (2) flood forecasting and monitoring, (3) risk communication and (4) flood risk governance.

Research inputs for this work were gathered during an international virtual dialogue that engaged 40 public servants, practitioners, academics and policymakers from Brazilian and British hazard and risk monitoring agencies during the Covid-19 pandemic.

The common challenges identified were lack of local data, data integration systems, data visualisation tools and lack of communication between flood agencies.

This work instigates an interdisciplinary cross-country collaboration and knowledge exchange, focused on tools, methods and policies used in the Brazil and the UK in an attempt to develop trans-disciplinary innovative ideas and initiatives for informing and enhancing flood risk governance.

This paper expands theory on strategists by investigating how non-executive strategy professionals in multi-business firms strategize. In focus is the strategizing of two groups of non-executive strategy professionals: a corporate strategy team and eleven business strategists employed in each of the incorporated units.

A case study design was employed to explore privileged accessed data to gain first-hand in-depth qualities of strategists' work. The design was characterized by phenomenon driven immersed participatory insider research with retrospective reflection and theorizing. Data includes strategies, interview data, calendars, meeting minutes, workshop material and observational field notes.

Non-executive strategy professionals in multi-business firms are either employed at the corporate center or in the peripheral businesses. Based on this location and their individual experiences they assume an exclusive content or an inclusive process strategizing orientation. In practice, the groups strategize tightly together.

Case studies are useful in explorative research providing thick descriptions. While empirically rich, the results of this study are limited by the context of one single case. Future research is encouraged to confirm, contradict and refine the results presented.

The insights from this study can help organizations regarding how to employ strategy professionals in multi-business firms.

This paper contributes to a recognized need to explore strategists' work. In contrary to the majority of existing research, focusing on senior management and/or strategy formulation, this paper highlighted non-executive strategy professionals' strategizing.

According to the significant growth of literature and continued adoption of people analytics in practice, it has been promised that people analytics will inform evidence-based decision-making and improve business outcomes. However, existing people analytics literature remains underdeveloped in understanding whether and how such promises have been realized. Accordingly, this study aims to investigate the current reality of people analytics and uncover the debates and challenges that are emerging as a result of its adoption.

This study conducts a systematic literature review of peer-reviewed articles focused on people analytics published in the Association of Business School (ABS) ranked journals between 2011 and 2021.

The review illustrates and critically evaluates several emerging debates and issues faced by people analytics, including inconsistency among the concept and definition of people analytics, people analytics ownership, ethical and privacy concerns of using people analytics, missing evidence of people analytics impact and readiness to perform people analytics.

This review presents a comprehensive research agenda demonstrating the need for collaboration between scholars and practitioners to successfully align the promise and the current reality of people analytics.

This systematic review is distinct from existing reviews in three ways. First, this review synthesizes and critically evaluates the significant growth of peer-reviewed articles focused on people analytics published in ABS ranked journals between 2011 and 2021. Second, the study adopts a thematic analysis and coding process to identify the emerging themes in the existing people analytics literature, ensuring the comprehensiveness of the review. Third, this study focused and expanded upon the debates and issues evolving within the emerging field of people analytics and offers an updated agenda for the future of people analytics research.

This study examines the relation between the presence of analysts’ long-term growth (LTG) forecasts and the post-earnings-announcement drift (PEAD).

Using a sample of firm-quarters from 1995 to 2013, the author conducts various regression analyses.

The author finds that the magnitude of PEAD is significantly smaller for firms with LTG forecasts. The relationship holds after controlling for a wide range of explanatory variables for PEAD returns or for the presence of LTG forecasts. The author further investigates three nonexclusive hypotheses to explain this relationship. First, LTG forecasts may convey incremental value-relevant information that facilitates investors’ processing of short-term earnings information. Second, the presence of LTG forecasts may indicate superiority in analysts’ short-term forecast ability and identify firms with more efficient short-term forecasts. Third, the presence of LTG forecasts may be associated with cross-sectional differences in the persistence of earnings surprises. The author finds that none of these fully accounts for the negative relationship between the presence of LTG forecasts and PEAD returns. Instead, the relationship may be a result of the presence of LTG forecasts capturing some unobservable firm characteristics beyond those identified in prior studies.

This study contributes to the PEAD literature by identifying a novel analyst-based predictor of the cross-sectional variation in PEAD returns.

The purpose of this paper is to introduce engaged scholarship as a method for addressing the “wicked problem” of High Street change through successful collaboration and co-production of knowledge between academics, practitioners, citizens and other place stakeholders.

The first part of this paper introduces engaged scholarship as a participatory form of research and situates it within the context of the High Street. The second part presents the case of High Street UK 2020 (HSUK2020) via Van de Ven’s diamond model of engaged scholarship as a guide.

Engaged scholarship’s focus on knowledge production and on collaboration between the research team and the community enabled us to improve the understanding of factors affecting High Streets amongst a diverse group of stakeholders and focus on what works for the towns. The ongoing impact of HSUK2020 on the project towns’ action plans and on this current research is testament to how engaged scholarship research can drive the agendas for both academics and communities.

The paper presents engaged scholarship as an alternative collaborative method of conducting research on the High Street, one that is more in line with the current trends in retailing and works as a motivating factor for community engagement.

Survivability capital is a unique resource resulting from the “familiness” constituting an inherent feature of family firms. Familiness represents the ability of family members to reinforce the financial and non-financial resources of businesses facing threats to their economic existence. This work proposes and examines various dimensions of the survivability capital construct, verifying whether family firms expecting deterioration of their economic situation or problems with survival due to the COVID-19 crisis can mobilise sufficient capital to survive.

This article provides empirical evidence based on a cross-sectional online survey of 167 Polish family firms, conducted at the beginning of the COVID-19 pandemic. The method (scale) of survivability capital measurement was elaborated and validated using principal component analysis (PCA) and confirmatory factor analyses (CFA). Next, the mobilisation of the different dimensions of survivability capital was examined using PLS-SEM modelling.

The survivability capital of family firms is composed of two dimensions: internal (based on directly involved family members) and external (based on not directly involved family members). Family firms facing crisis-induced deterioration of the economic situation engage its internal component. Subsequently, family firms forecasting decreasing probability of survival during a crisis try to engage both the internal and the external components of survivability capital. Such behaviour is in line with the resource-based view as well as with the sustainable family business theory.

To the best of the authors' knowledge, this is one of the first studies to examine analytically the survivability capital construct. While previous studies mentioned the existence of survivability capital, this study attempts to introduce its various dimensions and test the mobilisation of survivability capital during the COVID-19 crisis.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This paper aims to forecast inbound and outbound container throughput for Bangkok Port to 2041 and uses the results to inform the future planning and management of the port’s container terminal.

The data used cover a period of 16 years (192 months of observations). Data sources include the Bank of Thailand and the Energy Policy and Planning Office. Cause-and-effect forecasting is adopted for predicting future container throughput by using a vector error correction model (VECM).

Forecasting future container throughput in Bangkok Port will benefit port planning. Various economic factors affect the volume of both inbound and outbound containers through the port. Three cases (scenarios) of container terminal expansion are analyzed and assessed, on the basis of which an optimal scenario is identified.

The economic characteristics of Thailand differ from those of other countries/jurisdictions, such as the USA, the EU, Japan, China, Malaysia and Indonesia, and optimal terminal expansion scenarios may therefore differ from that identified in this study. In addition, six particular countries/jurisdictions are the dominant trading partners of Thailand, but these main trading partners may change in the future.

There are only two major projects that have forecast container throughput volumes for Bangkok Port. The first project, by the Japan International Cooperation Agency, applied both the trend of cargo volumes and the relationship of volumes with economic indices such as population and gross domestic product. The second project, by the Port Authority of Thailand, applied a moving average method to forecast the number of containers. Other authors have used time-series forecasting. Here, the authors apply a VECM to forecast the future container throughput of Bangkok Port.