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A growing body of research finds that gig economy platforms use gamification to enhance managerial control. Focusing on technologically mediated forms of gamification, this literature reveals how platforms mobilize gig workers’ work effort by making the labour process resemble a game. This chapter contends that this tech-centric scholarship fails to fully capture the historical continuities between contemporary and much older occurrences of game-playing at work. Informed by interviews and participatory observations at two food delivery platforms in Amsterdam, I document how these platforms’ piece wage system gives rise to a workplace dynamic in which severely underpaid delivery couriers continuously employ game strategies to maximize their gig income. Reminiscent of observations from the early shop floor ethnographies of the manufacturing industry, I show that the game of gig income maximization operates as an indirect modality of control by (re)aligning the interests of couriers with the interests of capital and by individualizing and depoliticizing couriers’ overall low wage level. I argue that the new, algorithmic technologies expand and intensify the much older forms of gamified control by infusing the organizational activities of shift and task allocation with the logic of the piece wage game and by increasing the possibilities for interaction, direct feedback and immersion. My study contributes to the literature on gamification in the gig economy by interweaving it with the classic observations derived from the manufacturing industry and by developing a conceptualization of gamification in which both capital and labour exercise agency.

This paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.

The authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.

The results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.

The effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.

Facebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.

The approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.

To the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.

Purpose: Analysis and systematisation of global trends in the transformation of DSSs from the standpoint of solving their global and local problems and determining the central axioms of setting up and supporting business processes in DSSs.

Need of the Study: Decision Support Systems (DSSs) are the basis of doing business in an enterprise by automating business processes, keeping accounting and reducing various risks associated with complexity, labour-intensiveness, slow execution time and, therefore, potential loss of profit. In recent decades, the rapid development of DSSs has led to the emergence of complex enterprise information system architectures. At the same time, many local business processes are not implemented or are partially implemented. In Ukraine, such techniques include VAT accounting.

Methodology: The study is based on the literature analysis, Internet resources and practical experience obtained during the SAP ERP system implementation projects. Particular attention is paid to developing information systems architecture to solve the problems enterprises face during their growth. Thanks to the analysis of the example of the realisation of the Internet sales process and the induction method, the axioms of automation of business processes in accounting systems were formed.

Findings: Regardless of the qualitative and quantitative transformation, modern DSSs still cannot solve all the enterprise’s problems, mainly due to the use of paper documents and the diversity of national legislation. By the example of the SAP ERP system, the optimal implementation of the business process of VAT liabilities was proposed by Ukrainian legislation for sales below cost price.

Practical Implications: Compliance with the established axioms of automation of business processes will reduce the cost of resources for their implementation, maintenance and correction of potential errors and, therefore, will provide an opportunity to process more transactions. Implementing the proposed algorithm for calculating VAT liabilities in SAP ERP for sales below the cost price will simplify the existing process and enable the fulfilment of other requirements within the framework of current legislation.

The primary aim of this study is to examine the decision-making process of a reverse supply chain within a cooperative-competitive environment, with a particular emphasis on the incorporation of Internet of Things (IoT) technology in the recycling process.

The impact of IoT on recycling effectiveness, RF (refurbished) product demand and remanufacturing expenses is analyzed. The game-theoretic models are constructed to incorporate the co-competitive relationship between a IR (Internet Recycler) and a remanufacturer, with specific attention paid to the IR's application of IoT technology.

The results indicate that the use of IoT reduces the costs associated with remanufacturing, leading to an increase in the transfer price of pre-owned items procured from IRs. The efficacy of the reverse supply chain depends not only on its cost-saving capabilities but also on consumer preferences toward RF and remanufactured (RM) items.

IoT technology can benefit Internet recycling and remanufacturing by enhancing transparency, quality monitoring and automation of processes. It can also help reduce operating costs, improve customer engagement and incentivize cooperation between organizations.

This study examined the strategic decision-making processes in the context of cooperation and competition between IR and remanufacturers. In addition, this research makes a valuable contribution to the literature by elucidating the optimal conditions for the effective implementation of Internet of Things (IoT) technology in recycling operations.

While the number of hybrid journals has increased with the conversion from subscription journals, article processing charges (APCs) have not been examined as frequently as gold open access journals. This study compared the factors affecting APCs for hybrid and gold open access journals by formulating a charge equation.

This study examined the APCs for 1,354 hybrid and gold open access journals in the Springer imprint. Using the ordinary least squares method, it investigated the determinants of charges, including the relationship between subscription prices and APCs for hybrid journals.

The results revealed that the charges set by hybrid journals were higher than those set by gold open access journals by US$1,620, after controlling for other variables. A reason could be the oligopolistic market structure of the leading publishers. Although the publisher imprint set the APCs based on the journal characteristics, the difference in the determinants of the charges between the two journal types may be due to the business models specific to the journal types.

The findings suggested that policymakers must consider the market power of leading publishers to establish a healthy scholarly communication market.

This study examined the relationship between subscription prices and charges for hybrid journals as well as the determinants of charges for both journal types, considering various characteristics of individual journals.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.