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1 – 10 of over 1000Jingrui Ge, Kristoffer Vandrup Sigsgaard, Julie Krogh Agergaard, Niels Henrik Mortensen, Waqas Khalid and Kasper Barslund Hansen
This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance…
Abstract
Purpose
This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.
Design/methodology/approach
The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.
Findings
Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.
Research limitations/implications
The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.
Originality/value
The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.
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Prashant Kumar, Khyati Shetty, Jason R. Fitzsimmons and Steven George Hayes
Rexford Abaidoo and Elvis Kwame Agyapong
This study examines how specific micro-level macroeconomic indicators influence corporate performance volatility among US corporate bodies in the short run.
Abstract
Purpose
This study examines how specific micro-level macroeconomic indicators influence corporate performance volatility among US corporate bodies in the short run.
Design/methodology/approach
The study employs error correction autoregressive distributed lagged (ARDL) model (ECM) to examine how micro-level variables influence volatility associated with corporate performance in the short run.
Findings
This paper finds that disaggregated or micro-level variables examined, tend to exhibit features that are not readily apparent from the aggregate variable from which such variables are derived. For instance, reported empirical estimate suggests that, growth in expenditures on services and nondurable goods tend to lower volatility associated with corporate performance, whereas government expenditures and expenditures on durable goods rather worsens volatility associated with corporate performance, all things being equal. Additionally, presented empirical estimates further provide evidence suggesting that macroeconomic uncertainty and inflation uncertainty significantly moderate or influence the extent to which disaggregated variables impact corporate performance volatility.
Originality/value
Compared to related studies in the reviewed literature, this study rather examines volatility associated with corporate performance instead of the corporate performance indicator itself. Additionally, this paper also examines how disaggregated variable instead of aggregate variables impact such volatility. Finally, the moderating role of key macroeconomic conditions in such a relationship is also examined.
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Cosimo Magazzino and Fabio Gaetano Santeramo
In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.
Abstract
Purpose
In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.
Design/methodology/approach
An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.
Findings
Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.
Originality/value
The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.
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Jinou Xu, Margherita Emma Paola Pero, Federica Ciccullo and Andrea Sianesi
This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the…
Abstract
Purpose
This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.
Design/methodology/approach
Based on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.
Findings
This study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.
Research limitations/implications
This paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.
Originality/value
This study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.
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Abstract
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Oluyemi Theophilus Adeosun and Kayode Ebenezer Owolabi
This paper aims to shed light on gender inequality in Nigeria exploring new available data. It makes a case for attention to women empowerment and likely economic outcomes. The…
Abstract
Purpose
This paper aims to shed light on gender inequality in Nigeria exploring new available data. It makes a case for attention to women empowerment and likely economic outcomes. The general objective of the research work is to ascertain the direction of gender inequality and show the pattern of inequality. Also, sectoral trends are obtained by analyzing and examining income inequality in Nigeria.
Design/methodology/approach
The paper obtained data from the Living Standard Measurement Survey Wave 3, published 2017 with emphasis on the earnings that accrued to both male and female. The study employed the ordinary least square (OLS) method to show the relationship between the mean income and other parameters such as the sector of employment, marital status and education level. Theil’s entropy index was used to measure the within and between inequality that exist in the economy and across regions and sectors while adopting the overcrowding theory.
Findings
The result shows that gender inequality is more pronounced across the region, location and in some sectors of employment than the others. Geographical area has a higher effect on earnings disparity but is more pronounced among females. Also, the result showed that gender within inequality was high in the regions, education, location, and marital status while a higher level of education contributes to high wages for women. However, married women are more deprived.
Originality/value
This study has further revealed the need to bridge the gap gender inequality has caused in Nigeria, especially related to income, education and geographical location, with a focus on both opportunities and outcomes.
Ricardo Vinícius Dias Jordão, Vander Ribeiro de Almeida and Jorge Novas
The purpose of this paper is to analyze the influence of intellectual capital (IC) on sustainable economic and financial performance (EFP) and value creation (VC) in Brazilian…
Abstract
Purpose
The purpose of this paper is to analyze the influence of intellectual capital (IC) on sustainable economic and financial performance (EFP) and value creation (VC) in Brazilian companies.
Design/methodology/approach
Based on finance and accounting theories, a quantitative and descriptive long-term study was carried out in the companies listed on the Brazil Stock Exchange and Over-the-Counter Market (B3), covering 20 years period.
Findings
The results indicate that IC positively influences profitability, corporate return and organizational value sustainably; the most intangible-intensive Brazilian companies listed on B3 presented more robust results than the least intangible-intensive; and IC contributes to a systematic increase in EFP and VC over time.
Research limitations/implications
Using a well-established metric, the IC-INDEX, the IC and its effects were measured, obtaining theoretical contributions (expanding the understanding of the IC influence in sustainable EFP and VC from a long-term perspective – one subject still unexplored in the literature); and empirical (increasing the understanding of the IC’s role as a driver of competitiveness, performance and organizational value).
Practical implications
This study increases the understanding of the theoretical and practical effects of IC, also providing a competitive benchmarking process to access sustainable EFP and VC of companies and their industries.
Originality/value
The originally applied and validated proposal extends existing theory by offering a set of indicators to scale the contribution of IC to competitiveness from the perspective of long-term (historical) corporate outcomes.
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Alberto Antonio Agudelo Aguirre, Néstor Darío Duque Méndez and Ricardo Alfredo Rojas Medina
This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average…
Abstract
Purpose
This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.
Design/methodology/approach
The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.
Findings
The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.
Research limitations/implications
Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.
Practical implications
The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.
Originality/value
This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.
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