Search results

This study investigates how asset specificity, relational governance and firm adaptability relate with supply chain integration (SCI), considering selected food processing firms (FPFs) in Uganda.

This study applies a quantitative research methodology. This research draws on a sample of 103 FPFs that have been selected from a population of 345 FPFs located in Kampala district. Hypothesis testing was done using Smart PLS version 3.

Asset specificity has a significant positive relationship with SCI, and firm adaptability partially mediates this relationship. Also, there is a full mediation impact of firm adaptability on the relationship between relational governance and SCI.

This study focused on perceptual measures to get responses from managers on the level of integration with key suppliers and customers, yet firms deal with a number of suppliers and customers.

This study contributes to existing literature on SCI by applying the transaction cost theory. The study focuses on the influence of asset specificity, relational governance and firm adaptability on SCI in the food processing sector. Literature on relational governance in supply chain using the transaction cost theory remains scanty. Few studies have also focused on firm adaptability as a mediator in the FPS with specific focus on Uganda, yet the sector is highly faced with uncertain events. The uncertain events in the sector and in developing countries call for adaptive strategies. Additionally, this study is the first to use firm adaptability to mediate the influence of asset specificity and relational governance on SCI more so in a developing country like Uganda where the FPS is one of the most important in the economy.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.

This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.

The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.

Large computational cost.

Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.

Simplify the setting of boundary conditions in the transient calculation.

Hazel Kyrk’s contribution is the most advanced formulation of the economics of consumption as a social phenomenon, an approach to the analysis of consumption that, originated from Veblen’s theory, was developed in the US in the early 20th century. This approach was part of a wider stream of empirical analyses of consumption expenditure that had begun more than a century earlier.

Along with elements that can be traced back to the neoclassical tradition, in Keynes’ analysis of consumption, we find original elements. The dependence of consumption expenditure on the level of income, which is essential for asserting the principle of effective demand, can also be found in a long tradition of empirical studies. In qualifying this relationship, Keynes uses theoretical elements echoing key insights of the economics of consumption as a social phenomenon. There is no documentary evidence that Kyrk or the economics of the social relevance of consumption came to Keynes’ attention. It is possible, however, to develop reasonable speculative considerations to argue a link between Keynes’ elaboration and both the empirical literature on the determinants of consumption and the economics of consumption as a social phenomenon.

Buying online has become a widespread and common activity for consumers, and, for many organizations, e-commerce has become a very profitable alternative to sell their products and services, also allowing them to leverage their strategy in new geographical markets immediately. Although the literature on the subject is comprehensive, there is a gap in identifying the holistic constructs that are the determinants of consumers' choice of an online store. This research resorts to an exploratory study, based on a nonsystematic literature review, seeking to identify these constructs. The results obtained allowed us to identify the following constructs: consumer behavior, customer experience, web content, catalog, terms and conditions, customer support, perceived value, trust, security and privacy, satisfaction, and loyalty. Customer experience, satisfaction, and loyalty constructs stand out from a strategic perspective.

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.

The current study examines the effects of race, agency and environment on traffic stops in rural and non-rural spaces.

Using traffic stop data collected in a Midwest US County from January 1, 2020 to December 31, 2021, the current study uses logistic regression to examine racial disparities in traffic stops.

The results indicate that police decision-making in traffic stops may be influenced by other factors besides a driver’s race or ethnicity. In other words, the police officer’s decision making in a traffic stop varies between small and large agencies as well as rural and non-rural places.

This study provides one of the few examinations of racial disparities in traffic stops in rural places.