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This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.

This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.

An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.

The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.

We examine yield spreads, defined as the difference between the yield to maturity of the risky bank bond and that of a risk-free bond with similar maturity and other characteristics, after controlling for market, liquidity and tax factors. We use senior bonds issued by banks since one of the goals of the Dodd–Frank Act (DFA) is to reduce the possibility of a full-fledged bailout of banks. If markets do not believe that banks will be bailed out, senior bondholders will bear a higher exposure to default risks, and such risk perceptions will be reflected in the yield spread levels.

We use generalized method of moments (GMM) for parameter estimation with standard errors corrected for autocorrelation and heteroskedasticity using the Newey–West (1987) procedure with five lags.

Our results indicate a discount of 133 basis points in yield spreads due to the TBTF or too-big-to-fail factor prior to the DFA. However, the market charges a net premium of 36 basis points for the TBTF factor immediately after the DFA (a total change of 169 basis points). We examine commercial banks and noncommercial banks (primarily investment banks and insurance firms) separately. For commercial banks, the discounts observed prior to the DFA changes to a premium after the DFA. For investment banks, the higher premium charged prior to the DFA is reduced after the DFA.

Not all banks issue bonds and not all issued bonds trade in the secondary market frequently.

After the Great Recession, there is a sustained effort across the globe, to remove the possibility of a bailout of very large banks. With systemic risk monitoring, improved capital regulation, stress testing and other regulations on banks and other shadow banking organizations, the question of whether market perceives implicit guarantee of very large financial institutions. We have examined a new security that is not treated as capital.

If taxpayer bailouts are avoided, such resources can be used for other developmental purposes. The moral hazard problems of bank manager are also reduced.

We are the first to exclusively examine the senior bonds issued by banks around the enactment of the DFA, 2010.

There is still an ongoing debate on the value relevance of capital structure and its determinants. Recently the issue has been explored in family firms after being explored in mature firms. This paper investigates the role of institutional investors and the firm's innovation activity in influencing the firm's decision and ability to acquire debt capital.

A large sample of 700 privately-held family firms in Italy from 2010 to 2019. Two analysis techniques are used: panel analysis and path analysis. The value of debt and the debt ratio are used as leverage measures. The value of patent (as a proxy for innovation) and institutional investor are the explanatory variables.

The results show that institutional investors have no relationship with financial leverage measures except when controlling for an interaction variable (Institutional investors × Lombardy region). The patent value is positively correlated with debt; however, the ratio patent-to-asset is negatively related to financial leverage indicating higher risk exposure. The nonlinearity test demonstrates a turning point when the relationship between patent value and debt inverts.

Firms should monitor their innovation activity since excessive innovation increases risk exposure and affects financing opportunities and value. The involvement of institutional investors does not always enhance value.

Existing literature focuses separately on family firm innovations and financial leverage as outcome variables, emphasizing the role of institutional investors in both fields by adopting agency theory and socioemotional wealth framework. In this study, the authors go further by merging both relationships, investigating the dynamics of the institutional-family firm innovation relationship in influencing the firm's capital structure. The authors contribute to the ongoing debate by providing original findings on capital structure, governance and innovation, supported by rigorous methods to enhance family firms' decision-making.

The study examines the impact of fiscal policy shocks on economic growth and income inequality in Ghana. This has become necessary because of the interdependence between growth and income inequality and the role fiscal policy plays in this relationship in the development process of a country. Thus, a study that investigates how government expenditure shock and tax revenue shock influence the relationship between economic growth and income inequality could assist policymakers to adopt the best policy mix to ensure income equity and sustained economic growth in Ghana.

It employs sacrifice ratio from structural VAR model using quarterly time series data from 1996 to 2019 on Ghana.

Our results show that government expenditure shock impacts economic growth, exchange rate and education positively and significantly in the long run. Also, tax revenue shock has a positive impact on income inequality, economic growth and education. The findings further show that there exists a trade-off between economic growth and income inequality in the long run.

The relationships between fiscal policy shocks, economic growth and income inequality have been extensively discussed among scholars. Understanding how these three macroeconomic variables are determined and their interrelationships are crucial for policymakers. This is because fiscal policy aids in both economic growth and income inequality. In the empirical literature, the emphasis has been on independently estimating the growth effects of fiscal policy or the distribution effects of fiscal policy, leaving out the existence of possible trade-off between economic growth and income inequality following a fiscal shock. To the best of our knowledge, no empirical study has been done on Ghana to empirically examine the trade-off between economic growth and income inequality as we do in this paper.

This study developed a theoretical model to test the relationship between digital capability and Industry 4.0 (I4.0) and its effect on innovation performance in small and medium-sized enterprises (SMEs).

The proposed theoretical model was evaluated using partial least-squares structural equation modeling and fuzzy-set qualitative comparative analysis. The data were obtained from a sample of 536 SMEs in Chile.

The proposed model presented two dimensions of digital capability: management and information and communication technologies (ICTs). Management models composed of enterprise resource planning and customer relationship management systems are essential for optimizing organizational management. Meanwhile, ICTs facilitate the smooth flow of information within an organization, leading to improved efficiency in production processes. I4.0 is encouraged by exposing SMEs to base technologies such as data analytics. These results confirm that I4.0 influences innovation performance.

SME managers should encourage the development of digital capabilities to transition toward I4.0, as this can make SMEs more competitive and innovative in changing and dynamic scenarios.

I4.0 adoption and the development of digital capabilities can directly affect employment and national economic growth.

Most studies focus on the organizational factors affecting SMEs’ I4.0 adoption. They do not, however, address the role played by current digital capability in I4.0 technology adoption and its effect on firms’ innovation performance.

Este estudio desarrolló un modelo teórico para probar la relación entre la capacidad digital y la Industria 4.0 (I4.0) y su efecto en el desempeño de la innovación en pequeñas y medianas empresas (PYME).

El modelo teórico propuesto se evaluó mediante el uso de modelos de ecuaciones estructurales de mínimos cuadrados parciales y análisis comparativo cualitativo de conjuntos difusos. Los datos se obtuvieron de una muestra de 536 pymes de Chile.

El modelo propuesto presenta dos dimensiones de la capacidad digital: la gestión y las tecnologías de la información y la comunicación (TIC). Los modelos de gestión compuestos por sistemas de planificación de recursos empresariales y de gestión de relaciones con los clientes son esenciales para optimizar la gestión organizacional. Por su parte, las TIC facilitan el flujo fluido de información dentro de una organización, lo que conduce a una mejora de la eficiencia en los procesos de producción. La I4.0 se fomenta exponiendo a las PYME a tecnologías de base como el análisis de datos. Estos resultados confirman que la I4.0 influye en el rendimiento de la innovación.

La mayoría de los estudios se centran en los factores organizativos que afectan a la adopción de la I4.0 por parte de las pymes, pero no abordan el papel que desempeña la capacidad digital actual en la adopción de la tecnología I4.0 y su efecto en el desempeño innovador de las empresas.

Los gestores de las PYMES deben incentivar el desarrollo de capacidades digitales para realizar la transición hacia la I4.0, ya que esto puede hacer que las PYMES sean más competitivas e innovadoras en escenarios cambiantes y dinámicos.

La adopción de la I4.0 y el desarrollo de capacidades digitales pueden afectar directamente al empleo y al crecimiento económico nacional.

The purpose of this paper is to identify different types of resource and capability configurations among Hungarian family and nonfamily firms and explore which compositions can be considered competitive. In a rivalrous, dynamic world, understanding which sets of resources and capabilities lead to a higher level of competitiveness is vital.

This paper is based on a quantitative competitiveness survey carried out between November 2018 and July 2019 in Hungary. The authors used the Firm Competitiveness Index (FCI) to measure competitiveness and the resource-based view (RBV) approach to understand which configurations of resources and capabilities are responsible for a higher level of competitiveness based on 32 variables. An exploratory factor and cluster analysis were conducted to analyze the ownership's effect on firm competitiveness. The final sample size contained 111 companies, of which 53 were identified as family and 58 as nonfamily firms.

Factor analysis reveals five factors determining resources and capabilities: “operational,” “leadership,” “knowledge management,” “transformation” and “networking.” Based on these factors, the cluster analysis identified five groups in terms of types of family and nonfamily firms: “Lagging capabilities,” “Knowledge-based leadership,” “Innovativeness and transformation-oriented management,” “Relationship-oriented management” and “Business operation-oriented management.” Results show that nonfamily businesses focus on operational and leadership capabilities, reaching a higher FCI than family businesses, which are likely to invest more in their networking, transformation and knowledge management capabilities.

By defining the different configurations family and nonfamily firms rely on to reach competitiveness, the paper applies an essential element to the Hungarian and Middle Eastern European contexts of family business research. The findings contribute to developing family business literature and point out specific resources and capabilities family firms should focus on to shift toward reaching a higher level of professionalization and competitiveness. The characterization of different types of competitiveness comparing family and nonfamily firms enables the firms to assess customized implications.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

The research aims to explore the dynamic relationship between digital service innovation (DSI), artificial intelligence (AI) and business performance (BPer) in service-based models with a focus on how AI-enhanced insights from service use and customer feedback can strengthen business strategies. The aims are to show that DSI and AI are key to driving growth and efficiency in the digital economy and to underscore AI’s role in utilizing contextual data to improve decision-making and business outcomes.

The study uses general structural equation modeling to analyze Spanish manufacturing firms, focusing on medium-sized enterprises and including both business-to-business and business-to-consumer orientations. Data are drawn from the Iberian Balance Analysis System [Sistema de Análisis de Balances Ibéricos (SABI)] database, complemented by a Qualtrics survey to assess the integration of AI in decision-making processes. The methodology is designed to evaluate the interplay between DSI, AI and BPer, with the aim of identifying actionable insights for service-based business orientations.

The study clarifies the relationships between DSI, AI and BPer, providing new theoretical and empirical insights. The findings confirm DSI's direct positive impact on performance and suggest AI’s nuanced mediating role, emphasizing the need for strategic DSI-AI integration in manufacturing firms for enhanced performance.

The research explains the synergistic bond between DSI and AI in boosting BPer and discovering how by-product data can be transformed into strategic insights.

This study advises manufacturing sector leaders to integrate DSI and AI for enhanced performance and competitive advantage, emphasizing the value of high-quality, contextual data for AI learning and decision-making.

Researchers will observe that the study confirms the positive impact of DSI on BPer, while also highlighting the significant role of AI in enhancing this effect.

Learning capability improves knowledge resources fosters innovative capabilities and firm competitiveness. The study aims to examine the human resource management (HRM) practice and employee creativity relationship using organizational learning capability (OLC) as a mediating variable.

Data were collected from 67 small-sized and 96 medium-sized firms. Confirmatory factor analysis was applied to establish construct validity and reliability. Structural equation modeling was used to evaluate the proposed model and hypotheses.

The results show that performance appraisal and employee creativity were positively related. Employee participation and employee creativity were positively related. Compensation and employee creativity were nonsignificantly related. OLC mediates the performance appraisal and employee creativity relationship. Similarly, OLC mediates the employee participation and employee creativity relationship. However, OLC did not mediate the compensation and employee creativity relationship.

Due to the research’s SME focus and cross-sectional data, the finding’s generalizability will be constrained.

The findings of the study would be useful to policymakers, stakeholders and management of SMEs in developing a supportive learning climate that promotes experiential and continuous learning cultures to ensure strategic capabilities, sustainable competitive advantage and innovativeness.

The study contributes to the extant literature on OLC, HRM practices and employee creativity by empirically evidencing that OLC mediates the performance appraisal, employee participation and employee creativity relationship.