Empirical Data Research Articles

Supply chain integration, competitive strategies and firm performance

Purpose This study aims to examine the interaction between different types of supply chain integration (SCI) and competitive strategies on firm performance. Design/methodology/approach The research method is quantitative. The study develops a structured model and a set of hypotheses based on the related theories. Empirical data is collected in a cross-sectional manner using an online survey to test the model and its hypotheses. The survey comprised standard scales to measure study variables. The scales had high reliability and validity and were commonly used in the literature. The study was conducted on Iranian firms working in steel and auto parts industries. Based on official reports, a total of 305 active companies were found in these two industries. Due to the small population size, all companies in the two industries were contacted for participation. Overall, 84 firms completed the survey. To test the research hypotheses, the authors used hierarchical regression analyses using SPSS 27 software. Findings Results showed that internal integration improves financial performance when firms follow a cost leadership strategy. Differentiation strategy and process integration interact positively and improve operational performance. Type of industry had no significant effect. The results add to the SCI literature by showing that a match between competitive strategy and integration type can influence both operational and financial performance. In total, firms should focus on the appropriate dimension of SCI based on their competitive strategy to improve the performance of the firm. Originality/value The impact of competitive strategy as a moderator on the SCI-performance relationship has not been widely addressed in the literature. In one study, Huo et al. (2014) investigated this relationship using data from 604 manufacturing companies in China. The results provided support for financial performance but not for operational performance. However, the influence of competitive strategies on the SCI performance may vary in different countries and contexts with dissimilar environmental, social and cultural characteristics. As several supply chain scholars have argued, understanding contextual conditions that influence the effect of best practices such as SCI on performance are critical

Heterogeneous impacts of the built environment on survival of customized bus services across the downtown and suburbs

Customized bus services (CBS) lines have been increasingly added in cities to better serve diversified travel needs. The notable variations in the built environment across the downtown and suburbs could result in wide differences in local customers' behaviors and preferences. The allocation of CBS stops thus needs to be considered separately in urban centers and peripheries to ensure the best performance, but such spatial heterogeneity is not specifically studied and justified in the literature. This study takes empirical data containing continuous CBS stops in Shanghai, China, as an example. Using a latent class binomial logit model, it investigates the effects of bus characteristics, geographic, and built environment elements on the survival of CBS stops in downtown and suburbs. The findings demonstrate that the performance of CBS systems is significantly influenced by factors including road density, population density, POI, sidewalk width, greenery area, ticket fare, and the type of CBS line (commute or general) across urban centers and peripheries. Specifically, two latent classes of CBS stops are found to have different effects depending on the stop's location or type of the linked areas, i.e. downtown or suburbs, on a CBS line.

The Effect of Tax Incentives on the Performance of Specialized and Sophisticated Small and Medium-Sized Enterprises: The Role of Innovation

Tax incentives have been widely used to promote the development of enterprises, especially Specialized and Sophisticated enterprises in Chian. This research endeavors to examine the effects of tax incentives by amalgamating established theoretical frameworks and empirical data, while taking into account the attributes of specialized and sophisticated SMEs. Innovation, serving as a fundamental driver of contemporary economic, technological, and social development, exhibits diverse definitions across various academic fields. This research attempts to explain the direct and indirect ways in which tax incentives influence enterprise performance through examining the relationship between tax incentives, innovation input and output, and enterprise performance. To achieve this, the research uses a multiple linear regression model to test the direct effect of tax incentives on the performance of specialized and sophisticated SMEs. Additionally, a three-step method based on Baron and Kenny (1986) is used to test the mediating role of innovation input between tax incentives and enterprise performance. Furthermore, hierarchical regression is applied to assess the moderating effect of innovation output on the relationship between tax incentives and enterprise performance. The empirical analysis in this article confirms that all hypotheses are valid.

Exploring Financial Risks in Relation to Performance of MPESA Agents in Rural Areas, Kenya

The growth of mobile banking services, specifically Mpesa, has transformed financial transactions in Kenya, particularly in marginalized and rural regions. Ever since Safaricom introduced Mpesa in 2007, it has enabled various financial transactions like money transfers, bill payments, savings, and loans, becoming vital to Kenya's financial infrastructure. In rural areas, Mpesa shops are at risk of facing financial challenges which can impact their operations and long-term success. This research investigates how liquidity, credit, and operational risks affect the financial performance of Mpesa outlets in rural Kenya. The mismatch between cash coming in and going out results in liquidity risks, which can cause service interruptions and unhappy customers. Credit risks, resulting from borrowers failing to repay loans on time or defaulting, put pressure on cash flow and the stability of finances. Operational risks like technical failures, fraud, and regulatory non-compliance pose additional threats to the functionality and customer trust of the shops. By thoroughly analyzing pertinent literature and empirical data, this research investigates the way in which these risks interplay and impact the economic sustainability of Mpesa shops. The results are intended to guide improved risk management methods and policy suggestions to strengthen the resilience and sustainability of Mpesa agents, guaranteeing ongoing availability of crucial financial services in rural Kenya.

A Causal Model of Learning Loss in the Midst of COVID-19 Pandemic Among Thai Lower Secondary School Students

<p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Cambria,serif"><span style="font-size:9.0pt">It is known that the COVID-19 pandemic led to learning losses among students both domestically and internationally. Therefore, situational and casual factors were examined to discover and understand them so that learning loss could be reduced or recovered from. This research aimed to: (a) study learning loss situation; and (b) develop and examine the causal model of learning loss among lower secondary school students affected by the pandemic. The sample included 650 Grade 7-9 students selected by multi-stage random sampling. The data was collected using a self-developing questionnaire as a research instrument. The data was analyzed using descriptive statistics, independent samples t-test, ANOVA, and structural equation modeling (SEM) through the LISREL program. The findings were: (a) Lower secondary school students had an average academic achievement learning loss at the moderate level with the highest mean of learning loss in mathematics (<em>M</em>=3.012, <em>SD</em>=1.074), and an average learning characteristics learning loss at the medium level (<em>M</em>=2.824, <em>SD</em>=0.842). Several situational factors had a different effect depending on the school size with a statistical significance of .05.; and (b) the causal model showed the learning loss of grade 7-9 students was consistent with the empirical data (χ2=46.885, <em>df</em>=34, <em>p</em>= .069, GFI=0.991, AGFI=0.964, CFI=0.999, RMSEA=0.024, SRMR=0.014).</span></span></span></p>

Inner fission barriers of uranium isotopes in the deformed relativistic Hartree-Bogoliubov theory in continuum

Abstract The inner fission barriers of the even-even uranium isotopes from the proton to the neutron drip line are studied with the deformed relativistic Hartree-Bogoliubov theory in continuum. A periodic evolution for the ground state shapes is shown with the neutron number, i.e., spherical shapes at shell closures N=126, 184, 258, and prolate dominated shapes between them. In analogy to the shape evolution, the inner fission barriers also exhibit a periodic behavior: peaks at the shell closures and valleys in the mid-shells. The triaxial effect to the inner fission barrier is evaluated using the triaxial relativistic mean field calculations plus a simple BCS method for pairing. With the triaxial correction included, good consistency in the inner barrier heights is found with the available empirical data.
Besides, the evolution from the proton to the neutron drip line is in accord with the results by the multi-dimensionally constrained relativistic mean field theory. A flat valley in the fission barrier height is predicted around the neutron-rich nucleus 318U which may play a role of fission recycling in the astrophysical r-process nucleosynthesis.

Novel approaches in geomechanical parameter estimation using machine learning methods and conventional well logs

ABSTRACT Today, geomechanics plays a crucial role in the oil industry, particularly in enhancing production and ensuring well stability. To achieve optimal results, accurate estimation of geomechanical parameters is essential. One of the low-cost and accurate methods for estimating geomechanical parameters is the use of intelligent methods. In this research, geomechanical parameters are estimated using conventional data logs using intelligent methods. The aim of this study is to introduce a new machine learning algorithm to estimate geomechanical parameters using conventional data logs in one of the hydrocarbon field wells in southwest Iran. In this article, the shear wave velocity and uniaxial compressive strength (UCS) were estimated using machine learning algorithms. Subsequently, other geomechanical parameters were calculated based on these estimated parameters derived from machine learning algorithms. For shear wave velocity (Vs) prediction using MLP and CLM (CNN+LSTM+MLP) algorithms, First, effective features were selected using the Auto-encoder deep learning algorithm. The selected features for Vs input into the algorithms were Vp, RHOB, CALIPER, and NPHI, and then the Vs is estimated with MLP and CLM algorithm. To evaluate the results, the model was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. The CLM algorithm consistently demonstrated superior performance across all datasets, including training, testing, and blind data sets. The R2 values for blind data were R M L P 2 = 0.8727 , R C L M 2 = 0.9274 , respectively. These outputs are crucial for estimating subsequent studies. Next, Elastic Young’s moduli and Poisson’s ratio were calculated, and the dynamic brittleness index was computed using dynamic Young’s modulus and Poisson’s ratio. Subsequently, UCS values were predicted using machine learning algorithms. Since there were 12 laboratory core samples of UCS available, UCS was initially calculated using relevant empirical relations and data from some available well logs. This process aimed to extrapolate these core results to cover the entire target depth range from 3551.072 to 3799.789 m. Subsequently, a relationship between UCS derived from well logs and laboratory results was established for the depths corresponding to where the laboratory samples were recorded. Following that, an autoencoder deep network was utilized to select effective features for predicting UCS. The selected features for UCS input into the algorithms were Vp, RHOB, and CALIPER. Subsequently, UCS was estimated using MLP and CLM algorithms. To evaluate the results, the model performance was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. Furthermore, the results were checked and evaluated using a set of statistical measures calculated for the train, test, and blind datasets, that R M L P 2 = 0.9305 , R C L M 2 = 0.9953 were obtained for blind UCS data. The results demonstrate that CLM achieves high accuracy in estimating these parameters, with deep learning algorithms showing higher determination coefficients and lower errors compared to MLP. Furthermore, UCS and tensile strength were calculated, followed by the computation of static brittleness index. The relationship between dynamic and static brittleness index was also investigated. Overall, the findings indicate that machine learning algorithms are robust and accurate methods for estimating Vs, UCS, and other geomechanical parameters using conventional logs.

Practical and empirical cognition of the cognitive self

This paper explores the different ways in which knowledge of ourselves as cognizers depends on the transcendental unity of apperception. First, since all cognition requires the transcendental unity of apperception, so does empirical knowledge of ourselves as cognizers. Second, since empirical cognizers are cognizers, they must possess the unity of self-consciousness required for cognition. Third, empirical psychology needs an object and the transcendental unity of apperception makes it possible to attach ‘I-think’ to representations of the empirical world, thereby supplying psychological states, such as ‘I think that bodies are heavy,’ to be studied. Finally, the Psychological Idea through which psychology is made systematic gets it first principle from the necessary unity of apperception.

Digital support of spatial planning processes in the regions

Subject. This article deals with the design and modeling of a data-centered tool for supporting spatial planning processes. Objectives. The article aims to develop a tool designed to solve the problem of automated formation of volumetric surfaces, which is necessary for assessing the innovative development of the region. Methods. For the study, we used the methods and approaches of the fuzzy logic theory, and logical programming methods. Results. An integrated approach to solving regional problems, taking into account the empirical knowledge of experts, helped develop a tool for assessing the innovative potential of the region, its approbation through using a Russian region as a case study is illustrated with graphs. Relevance. The formed triad of sustainable development makes it possible to reflect the behavior of the regional innovative development index as a whole on the basis of social, economic and environmental indicators of sustainability.

Unveiling the complex U‐shaped relationship between entrepreneurial orientation and exploratory/exploitative innovations: strategic planning as a moderator

Grounded in agency and stewardship theories, this study investigates the U‐shaped relationship between entrepreneurial orientation and exploratory/exploitative innovations and the moderating effects of strategic planning. Empirical data is collected from 396 A‐share listed public companies in China. Entrepreneurial orientation is assessed via internal R&D and external investment data, while content analysis captures exploitative, exploratory innovation, and strategic planning. The empirical findings yield two core conclusions: (1) This study finds the presence of U‐shaped relationships between entrepreneurial orientation and exploratory/exploitative innovations. As entrepreneurial orientation increases to an inflection point, it exhibits negative effects due to managerial agency behavior; however, beyond this point, it yields positive outcomes due to stewardship. (2) Strategic planning positively moderates the U‐shaped relationships, countering agency impact at low orientation levels while enhancing stewardship at higher levels. These insights advance entrepreneurial orientation research, agency vs. stewardship literature, and provide a roadmap for fostering exploratory and exploitative innovations through strategic integration of entrepreneurial orientation and leveraging of strategic planning.

Benchmarking and Optimization of Methods for the Detection of Identity-By-Descent in High-Recombining Plasmodium falciparum.

Genomic surveillance is crucial for identifying at-risk populations for targeted malaria control and elimination. Identity-by-descent (IBD) is increasingly being used in Plasmodium population genomics to estimate genetic relatedness, effective population size (N e ), population structure, and signals of positive selection. Despite its potential, a thorough evaluation of IBD segment detection tools for species with high recombination rates, such as P. falciparum , remains absent. Here, we perform comprehensive benchmarking of IBD callers - probabilistic (hmmIBD, isoRelate), identity-by-state-based (hap-IBD, phased IBD) and others (Refined IBD) - using population genetic simulations tailored for high recombination, and IBD quality metrics at both the IBD segment level and the IBD-based downstream inference level. Our results demonstrate that low marker density per genetic unit, related to high recombination relative to mutation, significantly compromises the accuracy of detected IBD segments. In genomes with high recombination rates resembling P. falciparum , most IBD callers exhibit high false negative rates for shorter IBD segments, which can be partially mitigated through optimization of IBD caller parameters, especially those related to marker density. Notably, IBD detected with optimized parameters allows for more accurate capture of selection signals and population structure; IBD-based N e inference is very sensitive to IBD detection errors, with IBD called from hmmIBD uniquely providing less biased estimates of N e in this context. Validation with empirical data from the MalariaGEN Pf7 database, representing different transmission settings, corroborates these findings. We conclude that context-specific evaluation and parameter optimization are essential for accurate IBD detection in high-recombining species and recommend hmmIBD for quality-sensitive analysis, such as estimation of N e in these species. Our optimization and high-level benchmarking methods not only improve IBD segment detection in high-recombining genomes but also enhance overall genomic analysis, paving the way for more accurate genomic surveillance and targeted intervention strategies for malaria.

Fabrication of an innovative phosphonate Schiff base adsorbent for molybdenum adsorption and its applications for molybdenum adsorption from spent hydrodesulfurization catalyst

AbstractBACKGROUNDTo investigate the recovery of molybdate from water and hydrodesulfurization catalysts, a novel synthesized phosphonate Schiff base, 4,4′‐(((2,2‐dimethylpropane‐1,3‐diyl) bis(azaneylylidene)) bis(methaneylylidene)) bis(2‐methoxyphenol) (HMI) and 4‐(((3‐((4‐(((S)‐hydroxyhydrophosphoryl)oxy)‐3‐methoxybenzylidene) amino)‐2,2‐dimethylpropyl)imino)methyl)‐2‐methoxyphenyl hydrogen phosphonate (HIMP), was utilized. This study aimed to assess the structural and property aspects of this synthesized compound using diverse characterizations, including FTIR, Thermogravimetric Analysis (TGA), mass spectrometry (GC–MS), fluorescence transfer spectra, scanning electron microscopy (SEM), and 1H‐NMR, 13C‐NMR, and 31P‐NMR.RESULTSThe study systematically examined several factors controlling molybdate recovery, including temperature, adsorbent dosage, pH, interaction time, and molybdate concentration using the batch technique. Optimal sorption effectiveness was achieved at pH 2, with an interaction time of 40 min, a 0.06 g adsorbent dose, at ambient temperature. The newly devised adsorbent exhibited an impressive adsorption capacity (Qe) of 372.54 mg of Mo per gram, with empirical data fitting the Langmuir and D‐R adsorption models. The kinetics of molybdate uptake followed second‐order kinetics. Thermodynamic aspects, including ΔG°, ΔS°, and ΔH°, were also investigated, providing appreciated perceptions into the energetic dynamics of the sorption process. Additionally, molybdate adsorption in the occurrence of other ions was explored, highlighting the selectivity of the modified phosphonate Schiff base for molybdate removal.CONCLUSIONThe study underscores the potential of the modified phosphonate Schiff base as a crucial adsorbent for molybdate elimination from both solutions and spent hydrodesulfurization catalysts. The findings offer important insights into the adsorption kinetics, thermodynamics, and selectivity of the adsorbent, enhancing our understanding of molybdate recovery processes and their broader applications. © 2024 Society of Chemical Industry (SCI).

Too hot to handle? Unthought anxiety and parallel process in social work supervision

ABSTRACT This paper explores isomorphic patterns in social work group supervision and social work practice through the lens of the psychoanalytical concept of parallel process and Bion’s theory of learning from experience. Empirical data from group supervision sessions are presented, and a link is theorised between sessions characterised by high levels of anxiety and the presence of parallel process, whereby ‘unthought’ anxiety provoking phenomena described in social workers’ practice appear to be repeated in supervision. Implications for supervision practice and social work more broadly are then discussed.

Zakat Maal Management and Regulation Practices: Evidence from Malaysia, Turki and Indonesia

This research investigates the management and regulatory practices of zakat maal (wealth tax) in Malaysia, Turkey, and Indonesia, aiming to provide a comparative analysis grounded in empirical data. Methodologically, the study employs a comparative qualitative analysis based on secondary data from academic literature, government reports, and institutional publications. The results show, first, Malaysia employs a centralized and technologically integrated approach, ensuring transparency and efficiency in zakat collection and distribution. In contrast, Turkey adopts a decentralized model driven by non-governmental organizations and community participation, fostering flexibility but posing challenges in standardization and oversight. Indonesia's hybrid model combines governmental oversight with private sector involvement, aiming to balance regulatory control with local adaptability, yet needs help in coordination and public trust. Second, the policy enhancements such as improved coordination mechanisms, strengthened regulatory frameworks, and enhanced public awareness to optimize Zakat's role in poverty alleviation and social welfare across Malaysia, Turkey, and Indonesia. This research contributes to the broader discourse on Islamic finance and social policy by providing nuanced insights into zakat management practices, offering valuable implications for policymakers, practitioners, and scholars interested in enhancing zakat efficacy globally.

When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data

Constraining the actions of AI systems is one promising way to ensure that these systems behave in a way that is morally acceptable to humans. But constraints alone come with drawbacks as in many AI systems, they are not flexible. If these constraints are too rigid, they can preclude actions that are actually acceptable in certain, contextual situations. Humans, on the other hand, can often decide when a simple and seemingly inflexible rule should actually be overridden based on the context. In this paper, we empirically investigate the way humans make these contextual moral judgements, with the goal of building AI systems that understand when to follow and when to override constraints. We propose a novel and general preference-based graphical model that captures a modification of standard dual process theories of moral judgment. We then detail the design, implementation, and results of a study of human participants who judge whether it is acceptable to break a well-established rule: no cutting in line. We then develop an instance of our model and compare its performance to that of standard machine learning approaches on the task of predicting the behavior of human participants in the study, showing that our preference-based approach more accurately captures the judgments of human decision-makers. It also provides a flexible method to model the relationship between variables for moral decision-making tasks that can be generalized to other settings.

Swedish student teachers’ perceptions of consequences of the teacher shortage for their future profession

The current teacher shortage is a global phenomenon, and it has spurred research from various perspectives. However, there is limited research on student teachers' perceptions, in particular in the Nordic countries. This study, therefore, is based on 75 student teachers' perceptions of the teacher shortage and its consequences for their future profession, their students, and society at large. Empirical data was analysed with reflexive thematic analysis and following Bronfenbrenner's ecological systems theory. The answers show great concern from all three perspectives. The conclusions are that the teacher shortage must be understood and addressed on a broad front that includes student teachers.

Hyperpersonal feedback and online romance fraud: an empirical examination

ABSTRACT Online romance fraud occurs when an offender uses deception to present a fake online identity to gain a victim’s affection and trust and then defrauds the victim in some way. Despite the crime’s serious impact, there is a lack of empirical knowledge regarding the processes by which it is carried out. Specifically, little is known about how the reciprocal interaction – or feedback – between online romance fraudsters and victims facilitates the crime. This study addresses this gap by using a randomized experimental design to explore how variations in potential reward presented by a fictional victim influence the strategies of fraudsters. To guide our examination, we draw from the hyperpersonal model of communication. Our analysis of sequential email exchanges with 94 online romance fraudsters reveals that their self-selective strategies are shaped by a feedback process between their messages and the victim’s impressions. We discuss implications for online romance fraud and the hyperpersonal model of communication.

ONeSAMP 3.0: Estimation of Effective Population Size via SNP Data from One Population.

The genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Methods that estimate Ne are important in population and conservation genetic studies as they quantify the risk of a population being inbred or lacking genetic diversity. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. We present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism (SNP) data collected from a single population sample using Approximate Bayesian Computation and local linear regression. We demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU license at https://github.com/AaronHong1024/ONeSAMP_3.

Acceleration of phosphorus weathering under warm climates.

The release of phosphorous (P) via chemical weathering is a vital process that regulates the global cycling of numerous key elements and shapes the size of the Earth's biosphere. It has long been postulated that global climate should theoretically play a prominent role in governing P weathering rates. Yet, there is currently a lack of direct evidence for this relationship based on empirical data at the global scale. Here, using a compilation of temperature and P content data of global surface soils (0 to 30 cm), we demonstrate that P release does enhance at high mean annual surface temperatures. We propose that this amplification of nutrient supply with warming is a critical component of Earth's natural thermostat, and that this relationship likely caused expanded oceanic anoxia during past climate warming events. The potential acceleration of phosphorus loss from soils due to anthropogenic climate warming may pose threats to agricultural production, terrestrial and marine ecosystems, and alter marine redox landscapes.

Acceleration of phosphorus weathering under warm climates.

The release of phosphorous (P) via chemical weathering is a vital process that regulates the global cycling of numerous key elements and shapes the size of the Earth's biosphere. It has long been postulated that global climate should theoretically play a prominent role in governing P weathering rates. Yet, there is currently a lack of direct evidence for this relationship based on empirical data at the global scale. Here, using a compilation of temperature and P content data of global surface soils (0 to 30 cm), we demonstrate that P release does enhance at high mean annual surface temperatures. We propose that this amplification of nutrient supply with warming is a critical component of Earth's natural thermostat, and that this relationship likely caused expanded oceanic anoxia during past climate warming events. The potential acceleration of phosphorus loss from soils due to anthropogenic climate warming may pose threats to agricultural production, terrestrial and marine ecosystems, and alter marine redox landscapes.