Research Handbook on Human Resource Management and Disruptive Technologies

This comprehensive Research Handbook examines the fundamental influence of emerging disruptive technologies, such as artificial intelligence, online platforms, the internet of things, and social robots, on Human Resource Management (HRM). Bringing together an array of interdisciplinary experts, this erudite Research Handbook analyses the HRM challenges posed by disruptive technologies and develops practical propositions to counteract them. It discusses the navigation of ethical dilemmas, human rights and digital governance in HRM, consumer value in the digital economy, and technology-driven changes in HRM practice. Featuring case studies on talent management in multinational enterprises, the engagement of digital generations with contemporary technologies, and the introduction of cobots in the manufacturing industry, contributors expertly explore current discussions and future directions for scholarly research. Promoting a better understanding of critical perspectives on HRM and disruptive technologies, this Handbook will be a vital resource for students, scholars and researchers in business analytics, information systems, knowledge management, organisational innovation, technology, and ICT. It will also provide valuable guidance on the effects of emerging technologies for HR and leadership practitioners

Digital at heart- How to lead the human centric digital transformation

The much-needed digital transformation organisations and companies must make creates tension and uncertainty for many. After all, more than finding and applying the latest technologies, it is essential to streamline internal processes and adapt the organization to new ways of working. Success largely depends on the willingness of all employees to participate in this process and, therefore to what extent organizations succeed in transforming the mindset and culture within the company. This book will teach you how to place people first in a digital transformation process. It allows you to look at the relationship between people and technology in a new way and helps get all employees on board confidently

Strategy in Turbulent Times. How to Design a Strategy That Is Robust and Future-Proof

Companies face increasingly turbulent times. Economic and political uncertainty, sustainability developments, and competitors with new business models are just some issues that stretch companies' resilience and adaptability. Strategy in Turbulent Times presents a way of analyzing and fighting turbulent environments. Using four animal metaphors, the Camel, Salmon, Chameleon and Octopus, it shows you how to develop new strategies and how to implement them. It is up to you to discover which animal represents the appropriate turbulence strategy for your organization

Unanswered questions in entrepreneurial finance

While the academic literature on entrepreneurial finance has expanded exponentially, many gaps in our knowledge remain. This is driven by digitalization impacting the development of new investment types such as crowdfunding and ICO, the emergence of new investors based upon digital technologies, and the functioning of existing investors. Next, the supply of entrepreneurial finance has become more diverse and new types of investors developed, like incubators and accelerators, family funds, impact investors, or sovereign wealth funds. This increases the sources and type of funding new ventures can get access to. Third, investors pay increasingly attention to non-financial goals like providing solutions to environmental or societal challenges. This paper explores these trends and suggests avenues for future research

Simplifying tree-based methods for retail sales forecasting with explanatory variables

A ‘simple’ tree-based framework is proposed for retail sales forecasting.• Our framework performs surprisingly well on two datasets with explanatory variables. Its good performance depends on collecting explanatory data and feature engineering. More sophisticated tree-based variants only marginally improve the forecast accuracy. Extensive simulation shows the benefit of our framework on inventory performance.Despite being consistently outperformed by machine learning (ML) in forecasting competitions, simple statistical forecasting techniques remain standard in retail. This is partly because, for all their advantages, these top-performing ML methods are often too complex to implement. We have experimented with various tree-based ML methods and find that a ‘simple’ implementation of these can (substantially) outperform traditional forecasting methods while being computationally efficient. Our approach is validated with a dataset of 4,523 products of a leading Belgian retailer containing various explanatory variables (e.g., promotions and national events). Using Shapley values and slightly adjusted tree-based methods, we show that superior performance depends on the availability of explanatory variables and additional feature engineering. For robustness, we show that our findings also hold when using the M5 competition dataset. Extensive numerical experimentation finally shows how the forecast superiority of our proposed framework translates to higher service levels, lower inventory costs, and improvements in the bullwhip of orders and inventory. Our framework, with its excellent performance and scalability to practical forecasting settings, we contribute to the growing body of research aimed at facilitating the higher adoption rate of ML among ‘traditional’ retailers

The importance of corporate reputation for sustainable supply chains: A systematic literature review, bibliometric mapping, and research agenda

Corporate Reputation (CR) is essential to value generation and is co-created between a company and its stakeholders, including supply chain actors. Consequently, CR is a critical and valuable resource that should be managed carefully along supply chains. However, the current CR literature is fragmented, and a general definition of CR is elusive. Besides, the academic CR debate largely lacks a supply chain perspective. This is not surprising, as it is very difficult to collect reliable data along supply chains. When supply chains span the globe, data collection is especially challenging, as the chain consists of multiple suppliers and subcontractors, positioned at different tier levels. Recognizing this, the paper examines firstly the current state of CR research through a systematic literature review from a business perspective. The review is combined with a bibliometric mapping approach to show the most influential research clusters, representative of CR research streams and their contributors. This process highlights that the connection between CR and supply chain issues represents a major research gap. Consequently, this paper introduces a research agenda connecting these the two traditionally separated research fields

Dual sourcing under non-stationary demand and partial observability

We study dual sourcing under stochastic and non-stationary demand. The non-stationarity is modeled through Markov-modulated changes in the underlying demand distribution. The actual demand distribution is not observed directly, yet demand observations reveal partial information about it. We propose a policy where a pre-committed base order from the slow source is complemented with flexible short-term orders from both the fast and slow source. The pre-committed order is cheaper, while flexible orders can be adjusted to the actual inventory needs and the non-stationary demand. By formulating the problem as a partially observable Markov decision process, we show that the optimal flexible orders follow an adaptive dual base-stock policy when the lead time difference between both sources is one period. A numerical validation study reveals how flexible slow source orders reduce the share of expensive orders from the fast source compared to a conventional tailored base-surge policy. In addition, our policy’s ability to adapt decisions to partial information allows for a more effective use of flexible orders. Our findings show the value of incorporating partial information to deal with the non-stationary demand and adding the flexible slow-sourcing option to create a more resilient replenishment policy

The Palgrave Encyclopedia of Private Equity

Private equity (PE)-backed buyouts are transactions in which a business or business units are acquired from its current shareholders by a PE investor together with the management team (Gilligan and Wright 2014; Renneboog and Vansteenkiste 2017), and in which the PE investor typically becomes the majority shareholder. PE investors are professional investors who invest in private companies with the aim to create value in the medium term, through enhancing governance, providing strategic advice and access to networks (Manigart et al. 2022). As they rely on the management team to execute the value creation process, agency problems may arise between PE investors as principals and the management as agents (Jensen 1989). To mitigate potential agency problems, elaborate contracts are negotiated between PE investors and management, but also between the buyers and the sellers...