Key Performance Drivers Research Articles

A Machine Learning Approach to Benchmarking Operators Performance: A New Perspective Utilizing Factor Contribution Analysis

Summary This study explains how production performance of the multifractured horizontal wells can be divided into two key contributing components: geographical location and completion strategy. Furthermore, we show how to quantify the contribution of these two independent components to production and to understand the variations in key performance drivers across the evaluated field. Being able to differentiate these contributions allows us to compare well performance in a consistent manner and identify potential upside opportunities, such as refracturing candidates, infill well development, and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach—a combination of XGBoost and Factor Contribution Analysis (FCA)—that not only allows for fieldwide well evaluations, but also provides a quantifiable contribution of each feature to production. Our approach generates a production prediction model and accounts for the completion parameters and geological information for each well. The final model can be used to either predict future performance of a field/well or to understand reservoir and completion characteristics. This study focuses on the latter and provides an approach to understand the main influencing factors behind well performance as a result of location and completion strategies. Our study is conducted on three major unconventional plays (Haynesville, Eagle Ford, and Bakken), where we demonstrate how different completion features (e.g. lateral length, proppant volume, and fluid volume) affect production data and what we could expect in terms of production should the well have been completed differently. We show how to combine the effect of individual controlling factors (e.g. location, depth, lateral length, proppant volume, fluid volume, and well spacing) to appropriately characterize the performance of each well in terms of two key components: location and completion. This enables us to quantify what portion of the production is a result of rock quality and how much is due to its completion strategy. This technique also allows us to quantify and relate each of these features, and highlight areas with desirable geological features, as well as good candidates for refracturing jobs. Moreover, we benchmark different operators’ performance as it relates to changing rock quality and completion strategies.

Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries?

The adoption of new technology is a key driver of firm performance and economic development. Yet the process of transferring and adopting new technologies is not seamless, particularly when the pace of technological change accelerates, and more advanced capabilities are required for the effective operation of new technology. In this paper we develop a framework for the analysis of the drivers of adoption of advanced digital production technologies associated with Industry 4.0 in developing economies. In particular, we ask whether firms’ participation to global value chains (GVCs) can facilitate the adoption of Industry 4.0 technologies in manufacturing sectors. Using a novel UNIDO database on firms’ adoption of different generations of production technology in Ghana, Viet Nam, and Thailand, we find that firms’ participation to GVCs is positively associated with the adoption of Industry 4.0 technologies, and that adoption is positively associated with firm-level performance.

Innovations and Hotel Performance in the Aftermath Announcement of Qatar Hosting FIFA 2022 World Cup

This article explores innovation processes in tourism within the context of Qatar Hotel sector. A theoretical framework, drawing on the existing literature, is developed to analyze innovation– performance relationships. Using the partial least squares-structural equation modeling technique for a sample of all the three- to five-star hotels in Qatar over 6 months between 2016 and 2017, the relationship between four innovations types and five types of performances are examined. The results show the existence of an innovativeness path beginning from organizational innovation leading to an improvement of the hotels' financial performance. Most specifically, organizational innovation is a key driver of innovative performance, while operational performance is the main driver of financial performance. Our research provides a better understanding of the innovation types driving hotels performance in the developing world, which may differ from the world developed countries. The findings of this study should inform policy discussions and the development of strategies to enhance innovation capacity among tourism enterprises.

Performance Implications of Organizational and Technological Innovation: An Integrative Perspective

Manufacturing firms engage in various innovation activities to achieve a sustainable competitive advantage. Although technological innovation is considered one of the key performance drivers, organizational innovation has become increasingly prominent. This study analyzes the relationship between organizational and technological innovation and examines their effect on firm performance in the manufacturing context. The empirical evidence is based on the European Manufacturing Survey (EMS)—Serbian data set gathered in 2018. Hypotheses on the relationship between organizational and technological innovations and firm performance are tested by structural equation modeling using data from 240 Serbian manufacturing firms. The results have shown that technological concepts strongly mediate the impact of organizational factors on firm performance (return on sales—ROS). Moreover, it could be said that, in contrast to the individual approach, to foster a ROS, a synergic effect of organizational and technological concepts is needed, implying the need for an integrative perspective in the process of innovation.

Are investments in material corporate social responsibility issues a key driver of financial performance?

AbstractIn this study, we address a lesser‐studied aspect of corporate social responsibility (CSR): distinguishing between investments in material versus immaterial CSR issues. The financial performance of stock portfolios formed according to material and general CSR issues is examined over the period 2007 to 2018 in the European Union and the United States. We find that materiality is relevant to finding the best and worst firms both in terms of CSR and financial performance, more significantly in the EU than in the US market. However, general CSR scores, including material and immaterial issues, do not discriminate the best firms.

Learning Paradox and Employee Performance: Empirical Evidence in a Vietnamese Public University

ABSTRACT The purpose of this study is to extend existing theory of organizational learning and develop a theoretical model that explores the role of exploratory learning, exploitative learning, and organizational learning capability (OLC) on individual employee performance in the higher education environment. The results of PLS-SEM analysis of data collected from 181 academics of a top public university in Ho Chi Minh City, Vietnam demonstrate that exploratory and exploitative learning are the key drivers of employee performance. Moreover, the findings shed light on the conditions that facilitate the contribution of exploratory and exploitative learning on employee performance. While balancing exploratory learning and exploitative learning in ambidextrous organizations creates synergy and fosters employee performance, the realization of this synergy requires the presence of OLC. The study also provides practical implications to assist higher education providers in public sector in understanding how learning mechanisms and OLC can work together towards enhancing academics’ performance.

Risks and profitability of microfinance institutions during crisis: empirical insights from an emerging market

The study explores the effects of different financial risks on the profitability of microfinance institutions (MFIs), amid a financial sector crisis. The study uses a pooled cross-sectional quarterly data covering 2016 to 2018 on MFIs in Ghana and a robust ordinary least squares regression. The study found a metamorphosis of the risk management strategies of MFIs. Overall, credit risk, capital risk and liquidity risk management are key drivers of performance. Initially, in 2016 and 2017, only credit risk was driving profits but by the end of the financial sector clean-up in 2018, all the four risk factors were driving performance. Size and asset turnover are other factors driving profitability. The study shows how important risk management is within the period of the financial crisis and how it affects microfinance performance. The findings also show how some prudential measures of the Bank of Ghana may have affected risk management and enhanced performance of MFIs after the financial sector clean-up exercise.

Predicting Market Inflation Expectations with News Topics and Sentiment

This study presents a novel approach to incorporating news topics and their associated sentiment into predictions of breakeven inflation rate (BEIR) movements for eight countries with mature bond markets. We calibrate five classes of machine learning models including narrative-based features for each country, and find that they generally outperform corresponding benchmarks that do not include such features. We find Logistic Regression and XGBoost classifiers to deliver the best performance across countries. We complement these results with a feature importance analysis, showing that economic and financial topics are the key performance drivers in our predictions, with additional contributions from topics related to health and government. We examine cross-country spillover effects of news narrative on BEIR via Graphical Granger Causality and confirm their existence for the US and Germany, while five other countries considered in our study are only influenced by local narrative.

Prediction across healthcare settings: a case study in predicting emergency department disposition

Several approaches exist today for developing predictive models across multiple clinical sites, yet there is a lack of comparative data on their performance, especially within the context of EHR-based prediction models. We set out to provide a framework for prediction across healthcare settings. As a case study, we examined an ED disposition prediction model across three geographically and demographically diverse sites. We conducted a 1-year retrospective study, including all visits in which the outcome was either discharge-to-home or hospitalization. Four modeling approaches were compared: a ready-made model trained at one site and validated at other sites, a centralized uniform model incorporating data from all sites, multiple site-specific models, and a hybrid approach of a ready-made model re-calibrated using site-specific data. Predictions were performed using XGBoost. The study included 288,962 visits with an overall admission rate of 16.8% (7.9–26.9%). Some risk factors for admission were prominent across all sites (e.g., high-acuity triage emergency severity index score, high prior admissions rate), while others were prominent at only some sites (multiple lab tests ordered at the pediatric sites, early use of ECG at the adult site). The XGBoost model achieved its best performance using the uniform and site-specific approaches (AUC = 0.9–0.93), followed by the calibrated-model approach (AUC = 0.87–0.92), and the ready-made approach (AUC = 0.62–0.85). Our results show that site-specific customization is a key driver of predictive model performance.

Sample Selection Bias in Evaluation of Prediction Performance of Causal Models.

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [5]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance and compare models on this new set. In this setting, the causal models have similar or worse performance compared to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.

INFLUENCE OF RESOURCE ALLOCATION AND INNOVATION ON PROFITABILITY OF MICRO SMALL AND MEDIUM ENTERPRISES (MSMEs) IN IN KENYA: A SURVEY OF KAMUKUNJI IN NAIROBI CITY COUNTY

This paper sought to explore the strategic management practices and profitability of micro small and medium enterprises. The main focus of the study was to assess influence of resources allocation and innovation on the profitability of micro, small and micro enterprises. The study adopted descriptive design where the population of the study was 1123 MSMEs and stratified random sampling techniques was applied to pick a sample of 112 MSMEs operating in central district business in Nairobi. The researcher used questionnaire to collect data. Moreover, a total of 112 questionnaires were distributed to business owners and managers of MSMEs in operating in Kamukunji in Nairobi County. Data was analyzed using both inferential statistics through regression, and descriptive statistics in the form of frequencies and percentages. The study found out that resource allocation positively and significantly influenced the profitability of MSMEs, innovation was found to positively and statistically significant influence on profitability of MSMEs, the study established that strategic leadership had a positive and significant effect on profitability of MSMEs. In addition, the study also established that strategic human resource was found to be positively related and statistically significant to profitability of micro small and medium enterprises. The study concluded that resource allocation and innovation had a significant effect on profitability of MSMEs. The study recommends that MSMEs should establish effective management and optimization of resource allocation since enterprises do not have unlimited access to resources. Moreover, study recommends that MSMEs should embrace various form innovations including product innovation, marketing innovation, process innovation and organization innovation which a considered as key driver of performance of MSMEs so that they can compete effectively with larger firms.

Artificial intelligent towards hotels’ competitive advantage. An exploratory study from the UAE

PurposeHotel industry is witnessing a radical change as a result of technology interaction such as artificial intelligence (AI) in service tasks. As a result of this transformation, the pattern of service delivery based on human interaction has been changed to digital interaction. This brought opportunities to the hotels industry and consolidate its competitive advantage. However, the understanding gap still existed in both practical implementation and literature, especially in developing countries. Therefore, this paper aims to explore how hotels use AI to carry out services tasks.Design/methodology/approachThe authors adopted the qualitative research method through Semi interviews. The sample was purposively selected from five 5-star hotels in the UAE, meanwhile, the managers were the targeted respondents.FindingsThrough content analysis, the authors find that the UAE hotels use AI in managing trip planning, reception service and room services. The authors also find that there are four key drivers that improve the performance of AI, which include AI infrastructure flexibility, strategic alignment, management and skills. Finally, the authors found four indicators of the impact of AI on hotels, which are quality, cost and market share and customers satisfaction.Originality/valueThis study is one of few studies that explored the use of AI in the hotels industry and discussed how AI influence several aspects of hotels performance and helped them to attain their competitive advantage. This study is also one of few studies and the first study in UAE to explore the key drivers of AI performance in UAE hotels.

Quantitative decision making in animal health surveillance: Bovine Tuberculosis Surveillance in Belgium as case study.

Despite eradication and control measures applied across Europe, bovine tuberculosis (bTB) remains a constant threat. In Belgium, after several years of official bTB-free status, routine movement testing, as currently practiced, revealed itself inadequate to detect some herds affected by sporadic breakdowns. The aim of this study was to assess different surveillance system components that strike a balance between cost and effectiveness and to identify sustainable alternatives, which substantiate a bTB-free claim while ensuring early detection and acceptance by various animal health stakeholders. For this pupose, a stochastic iteration model was used to simulate the current surveillance system's expected performance in terms of detection sensitivity and specificity. These results were then descriptively compared with observed field results. Second, the cost and effectiveness of simulated alternative surveillance components were quantified. Sensitivity analyses were performed to measure key assumptions' impacts (i.e. regarding diagnostic tests and true prevalence). The results confirmed discrepancies between the observed and simulated expected performance of bTB surveillance in Belgium. Second, simulated alternatives showed that interferon gamma (IFN-γ) and serological testing with antibody-enzyme linked immunosorbent assay (Ab-ELISA) targeting at-risk herds would enable an increase in the overall cost effectiveness (sensitivity and specificity) of the Belgian bTB surveillance system. Sensitivity analyses showed that results remained constant despite the modification of some key assumptions. While the performance of the ongoing bTB surveillance system in Belgium was questionable at the time of the study, this exercise highlighted that not only sensitivity but specificity also are key drivers of surveillance performance. The quantitative approach, taking into consideration various stakeholders' needs and priorities, revealed itself to be a useful tool in allowing evidence-based decision making for future tuberculosis surveillance in Belgium, in line with the international standards.

In the COVID era, business leaders must put inclusivity above expediency

Purpose This paper aims to identify the management practices and priorities best suited to present-day workforce needs. Design/methodology/approach From 19 March to 6 April 2021, Dare Worldwide conducted linear regression analysis aimed at understanding the key drivers of employee performance. The target population surveyed was: US adults who are employees of companies with at least 500 employees with a job title of manager or higher within selected industries (manufacturing, information, financial services, professional and business services, leisure and hospitality, wholesale and retail, education, health and government). Findings The research showed 85% of performance indicators is not linked to company performance and that employees value trust, engagement and communication. Businesses favour rapid adaptation to cope with crises and technology to drive job performance. In the survey, the highest performing factors for business were related to old-fashioned notions of expediency, none of which will impact company performance over the next five years. Employees meanwhile valued inclusivity and collaboration over expediency. Originality/value The research showed that c-suits are focussing on the wrong managerial processes and priorities to be successful over the long term. Helping employees understand and shape the purpose of their company will be critical as employees evaluate their motivations post COVID. The Dare Worldwide 2021 findings have implications for how senior management teams relate to their staff in an era of remote working and changing employee values and priorities.

МОДЕЛЮВАННЯ ОПТИМАЛЬНИХ РІВНІВ КОНКУРЕНТОСПРОМОЖНОСТІ СІЛЬСЬКОГОСПОДАРСЬКИХ ПІДПРИЄМСТВ В УМОВАХ СТАЛОГО РОЗВИТКУ

The article seeks to explore major challenges in the agricultural sector development in Ukraine. Using multifactor statistical analysis of the key drivers of agribusiness operation and performance, based on a matrix of 25 selected agribusiness development indicators for enterprises specialized in crop raising and 16 selected development indicators for livestock enterprises, similar developmental trends across Ukrainian regions have been revealed. According to the results of the analysis, certain taxonomic groups (clusters) were identified that allowed to display similar regional conditions within each group along with specifying regions with crop and livestock farming within one cluster, represented by Lviv, Ivano-Frankivsk and Rivne regions. For further research, agribusinesses were selected out of the above regions and their competitive positions were analyzed. A fuzzy-set method was used to identify the impact of sustainable development, in particular the level of economic, social and environmental sustainability of regions on the competitive position of agribusinesses. The input parameters are represented by economic, environmental and social sustainability indices, each of them is assigned specific characteristics and classified. In addition, using variance analysis, key reference indicators were calculated and the boundaries were set. The output data are determined by the level of agribusiness competitiveness in Lviv, Ivano-Frankivsk and Rivne regions (selected by the results of cluster analysis). According to the calculations results, linguistic evaluation was performed, based on variance analysis of terms. Modeling of the surface of the forecast values was carried out which revealed that the above the average level of agribusiness competitiveness is accompanied by a high level of economic sustainability, average level of environmental sustainability and the below average social stability level.

Entrepreneurship Competence and SME Sustainability in Kampala, Uganda

This study was carried out to establish the possibility of enhancing SME sustainability in Kampala, Uganda through boosting key internal drivers of entity performance (records management, internal communication, family influences, and online presence). Using conclusive, cross-sectional, and case study research designs supported by the philosophical underpinnings of critical realism and positivism epistemology, data was collected from 278 SMEs using semi-structured questionnaires. Using SEM analysis, it was revealed that all the key drivers have a positive significant statistical effects of the sustainability of SMEs (records management [S.E = 0.3; p = 0.000], internal communication [S.E = 0.229; p = 0.017], family influences [S.E = 0.162; p = 0.041], and online presence [S.E = 0.321; p = 0.000]). It was therefore recommended that key internal success factors identified need to be considered in the management and operations of SMEs in Kampala. This is aimed at guaranteeing the sustainability of SMEs and the boosting of the private sector.

The influence of habitat association on swimming performance in marine teleost fish larvae

AbstractLatitude and body size are generally considered key drivers of swimming performance for larval marine fishes, but evidence suggests that evolutionary relationships and habitat may also be important. We used a comparative phylogenetic framework, data synthesis and case study approach to investigate how swimming performance differs among larvae of fish species across latitude. First, we investigated how swimming performance changed with body length, and we found that temperate reef fishes have the greatest increases in swimming performance with length. Secondly, we compared differences in three swimming performance metrics (critical swimming speed, in situ swimming, and endurance) among post‐flexion larvae, whilst considering phylogenetic relationships and morphology, and we found that reef fishes have higher swimming capacity than non‐reef (pelagic and non‐reef demersal) fishes, which is likely due to larger, more robust body sizes. Thirdly, we compared swimming performance of late‐stage larvae of tropical fishes with oceanographic data to better understand the ecological relevance of their high‐capacity swimming. We found that reef fishes have high swimming performance and grow larger than non‐reef fish larvae, which we suggest is due to the pressures to find a specific, patchily distributed habitat upon which to settle. Given the current bias towards studies on percomorph fishes at low latitudes, we highlight that there is a need for more research on temperate reef fish larvae and other percomorph lineages from high latitudes. Overall, our findings provide valuable context to understand how swimming and morphological traits that are important for dispersal and recruitment processes are selected for among teleost fish larvae.

Eliminating the negative impacts of the Basel IV output floor by adjusting a bank’s business model

The purpose of this paper is to show the interaction between the Basel IV output floor and business model management. Specifically, the paper analyses how banks can optimise the output floor by moderately adjusting the composition of their portfolio. The individual topics are explained based on simplified exemplary cases. The presented capital floor analysis may help a bank’s top management to allocate the available capital better, formulate a coherent internal risk appetite, including the cost of capital in their pricing models, and set explicit targets for key performance drivers directly linked to the desired shareholder returns. With a target business model in mind, our approach can therefore be used to determine target levels for the individual risk positions that contribute to the output floor. The paper presents a procedure for overall bank management, particularly for business model planning in the presence of the Basel IV floor. Managers, analysts and regulators can apply our approach to analyse the business model of an individual bank, as well as the output floor of the banking sector as a whole. To our knowledge, our paper is the first academic contribution on the impact of the new prudential floor approach on the banks’ business model.

Examining the Determinants of Healthcare Workers' Performance: A Configurational Analysis during COVID-19 Times.

The evaluation of the work performance of health professionals has focused the interest of scientific research in recent decades as a basis for improving the quality of health services. The global COVID-19 pandemic has pushed countries’ health systems to the limit and had previously unknown consequences on the job performance of health professionals. In this context, what are the determinants of performance? There are numerous studies that link job performance with other variables that directly affect it, such as leadership, job satisfaction, organizational commitment, and work environment. However, there are no studies that jointly relate all these variables, and even less in the field of health. The main objective of this work is to analyse how these variables are configured together to generate a good level of performance of health professionals during the times of COVID-19. To do this, a fuzzy set qualitative comparative analysis (fsQCA) is carried out, an appropriate method that will allow finding the joint causal effects of key variables in human resources to ensure a good level of job performance in health organizations. The study reveals that leadership and commitment are the two key drivers of performance. The data confirm that the “recipe” to achieve a good level of performance consists of the combination of leadership, commitment, and a good work environment. Additionally, in the case of less satisfied workers, linking leadership and commitment is a sufficient condition.

Mapping the themes and intellectual structure of customer engagement: a bibliometric analysis

PurposeCustomer engagement (CE) as a domain of research started gaining impetus when it became apparent that it can be a key driver of a firm's performance, competitive advantage and loyalty. The purpose of this study is to develop a deep understanding of the CE construct in marketing literature using bibliometric analysis.Design/methodology/approachIn this study, 940 articles were retrieved from Scopus, the well-known electronic database. Bibliographic coupling and co-occurrence analysis using VOSviewer along with content analysis were employed.FindingsAfter careful content analysis, six clusters were identified through bibliographic coupling: (1) modeling customer engagement, (2) customer engagement theory and empirical validation, (3) customer engagement and service-dominant logic, (4) customer engagement and social media, (5) customer engagement and brand platforms and (6) engagement in other contexts. The outcomes of this study would not only be valuable for scholars working in the CE domain, but could also be useful for practitioners and policymakers who wish to enhance their understanding about CE.Originality/valueOver the past decade, the research on CE construct has exploded owing to the growing interest of both scholars and practitioners in the field. Despite being a popular field of research, there is no published work on a comprehensive bibliometric analysis of the construct in marketing literature. The current study bridges this gap in the existing literature.