Temporal range filtering is a critical operation in large-scale search systems, particularly for location-based services that need to filter businesses by operating hours. Traditional approaches either suffer from poor query performance (scope filtering) or index size explosion (minute-level indexing). We present Timehash, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision. Timehash employs a flexible multi-resolution strategy with customizable hierarchical levels. Through empirical analysis on distributions from 12.6 million business records of a production location search service, we demonstrate a data-driven methodology for selecting optimal hierarchies tailored to specific data distributions. We evaluated Timehash on up to 12.6 million synthetic POIs generated from production distributions. Experimental results show that a five-level hierarchy reduces index terms to 5.6 per document (99.1% reduction versus minute-level indexing), with zero false positives and zero false negatives. Scalability benchmarks confirm constant per-document cost from 100K to 12.6M POIs, while supporting complex scenarios such as break times and irregular schedules. Our approach is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.
The purpose of the study was to investigate factors affecting the career satisfaction of vendors’ employees who perform project tasks on-site at clients’ owned facilities for several years as part of project delivery in Sri Lanka. The study identified six factors that influence the career satisfaction of these employees, namely, work content, financial rewards and other gains, work relationships, supervisor support, general working conditions, and opportunities experienced due to being stationed at clients’ firms. Overall, the study context is novel and the findings make contributions to the advancement of project management literature and practice.
Maarten P. Scholl, Mahmoud Mahfouz, Anisoara Calinescu
et al.
While investment funds publicly disclose their objectives in broad terms, their managers optimize for complex combinations of competing goals that go beyond simple risk-return trade-offs. Traditional approaches attempt to model this through multi-objective utility functions, but face fundamental challenges in specification and parameterization. We propose a generative framework that learns latent representations of fund manager strategies without requiring explicit utility specification. Our approach directly models the conditional probability of a fund's portfolio weights, given stock characteristics, historical returns, previous weights, and a latent variable representing the fund's strategy. Unlike methods based on reinforcement learning or imitation learning, which require specified rewards or labeled expert objectives, our GAN-based architecture learns directly from the joint distribution of observed holdings and market data. We validate our framework on a dataset of 1436 U.S. equity mutual funds. The learned representations successfully capture known investment styles, such as "growth" and "value," while also revealing implicit manager objectives. For instance, we find that while many funds exhibit characteristics of Markowitz-like optimization, they do so with heterogeneous realizations for turnover, concentration, and latent factors. To analyze and interpret the end-to-end model, we develop a series of tests that explain the model, and we show that the benchmark's expert labeling are contained in our model's encoding in a linear interpretable way. Our framework provides a data-driven approach for characterizing investment strategies for applications in market simulation, strategy attribution, and regulatory oversight.
Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asymmetry in small price changes should vary over the business cycle. It should diminish during recessions and strengthen during expansions. We test this prediction using a large US store-level dataset with more than 98 million weekly price observations for the years 1989-1997, which includes an 8-month recession period, as defined by the NBER. We compare price adjustments between periods of recession - high unemployment, and expansion - low unemployment. Focusing on small price changes, we find, consistent with our hypothesis, that there is a greater asymmetry in small price changes during periods of low unemployment compared to the periods of high unemployment, implying that firms price-setting behavior varies over the business cycle.
Jeroen Middelhuis, Zaharah Bukhsh, Ivo Adan
et al.
Resource allocation plays a critical role in minimizing cycle time and improving the efficiency of business processes. Recently, Deep Reinforcement Learning (DRL) has emerged as a powerful technique to optimize resource allocation policies in business processes. In the DRL framework, an agent learns a policy through interaction with the environment, guided solely by reward signals that indicate the quality of its decisions. However, existing algorithms are not suitable for dynamic environments such as business processes. Furthermore, existing DRL-based methods rely on engineered reward functions that approximate the desired objective, but a misalignment between reward and objective can lead to undesired decisions or suboptimal policies. To address these issues, we propose a rollout-based DRL algorithm and a reward function to optimize the objective directly. Our algorithm iteratively improves the policy by evaluating execution trajectories following different actions. Our reward function directly decomposes the objective function of minimizing the cycle time, such that trial-and-error reward engineering becomes unnecessary. We evaluated our method in six scenarios, for which the optimal policy can be computed, and on a set of increasingly complex, realistically sized process models. The results show that our algorithm can learn the optimal policy for the scenarios and outperform or match the best heuristics on the realistically sized business processes.
Gerhard Zeisler, Tim Tobias Braunauer, Albert Fleischmann
et al.
The widely adopted Business Process Model and Notation (BPMN) is a cornerstone of industry standards for business process modeling. However, its ambiguous execution semantics often result in inconsistent interpretations, depending on the software used for implementation. In response, the Process Specification Language (PASS) provides formally defined semantics to overcome these interpretational challenges. Despite its clear advantages, PASS has not reached the same level of industry penetration as BPMN. This feasibility study proposes using PASS as an intermediary framework to translate and execute BPMN models. It describes the development of a prototype translator that converts specific BPMN elements into a format compatible with PASS. These models are then transformed into source code and executed in a bespoke workflow environment, marking a departure from traditional BPMN implementations. Our findings suggest that integrating PASS enhances compatibility across different modeling and execution tools and offers a more robust methodology for implementing business processes across organizations. This study lays the groundwork for more accurate and unified business process model executions, potentially transforming industry standards for process modeling and execution.
We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.
In statistical analysis, many classic results require the assumption that models have finite mean or variance, including the most standard versions of the laws of large numbers and the central limit theorems. Such an assumption may not be completely innocent, and it may not be appropriate for datasets with heavy tails (e.g., catastrophic losses), relevant to financial risk management. In this paper, we discuss the importance of infinite-mean models in economics, finance, and related fields, with recent results and examples. We emphasize that many results or intuitions that hold for finite-mean models turn out to fail or even flip for infinite-mean models. Due to the breakdown of standard thinking for infinite-mean models, we argue that if the possibility of using infinite-mean models cannot be excluded, great caution should be taken when applying classic methods that are usually designed for finite-mean cases in finance and insurance.
Ximena Guillén Vivas , Judith Galarza López , Eugenio Radamés Borroto Cruz
et al.
Ante el escenario actual, caracterizado por sudinamismo e incertidumbre, las Instituciones de EducaciónSuperior latinoamericanas deben actuar rápidamente paraenfrentar los retos que se derivan de las crecientes demandas.Se afronta la necesidad de desarrollar procesos que coadyuvena la mejora continua de la calidad, donde se encuentran losrelacionados con la acreditación y evaluación institucional. Enmedio de esta compleja realidad, la Universidad San Gregoriode Portoviejo ha desarrollado varios procesos. Esta investigacióntuvo como objetivo: evaluar la práctica de los procesos deacreditación y evaluación institucional desarrollados en lainstitución, entre el 2015 y el 2019. Se empelaron métodosteóricos (análisis-síntesis e inducción-deducción) y empíricos(encuesta y entrevista realizadas a autoridades universitarias,docentes y personal implicado en los procesos de Autoevaluacióny Acreditación de la Institución). Como resultado, quedóevidenciado que la universidad acumula experiencia en eldesarrollo de procesos vinculados con la gestión de la calidad;no obstante, también se precisaron algunas áreas de mejora quedeben ser atendidas, en el afán de continuar avanzando hacia laobtención de resultados e impactos de mayor relevancia social.
Mohammad Amin rashidi, Mohammad Mubin Shafii Natiq, Soroush Shaygan Nariman
Digital transformation has transformed many industries in recent years. One of the consequences of digital transformation can be mentioned the change of structures, procedures and processes. The wave of digital transformation has also caused fundamental changes in the banking industry. Digital banking is the provision of banking services based on digital technologies. Iran's banking industry has turned to digital services in recent years and this industry can be considered one of the leading industries in the country. However, there are strategic entry barriers in the way of banks to join the wave of digital transformation and sometimes it prevents them from joining this wave. The present study examines the barriers to strategic entry into the digital banking industry in Iran using the meta-composite method. In a period of ten years from 1391 to 1401, this research has examined the researches conducted in this field and has extracted the most important barriers to strategic entry into the digital banking industry. Research studies show that barriers to strategic entry into the digital banking industry in Iran are divided into two general categories: "internal" and "external" and factors such as: cultural, managerial, technological, financial, political-legal and skill barriers. They are effective.
Entrepreneurship is a dynamic and multifaceted endeavour and the entrepreneurs must possess or develop a wide range of combination of personal and professional characteristics in order to be successful in their initiatives. These features are commonly associated with individuals who embark on the challenging and rewarding journey of starting and growing a business. In an interconnected world, a dynamic environment can bring both opportunities and challenges for SMEs. Organizations need to consider the global context when making strategic decisions and managing change, based on a systemic vision. This paper deals with change management for SMEs by thoroughly analysing and understanding the context of change, the individuals and organizations that are involved, being able to design and develop more informed strategies, anticipate potential obstacles, and implement effective solutions. This proactive approach enhances for the entrepreneurs the likelihood of successful adaptation and growth in the face of ongoing and dynamic change.
Economics as a science, Business records management
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implies a need for extensive efforts in market representation, behavior objectives, and training processes, which have often been lacking in previous works. We propose a training and evaluation process to assess the performance of classical DRL algorithms for portfolio management. We found that most Deep Reinforcement Learning algorithms were not robust, with strategies generalizing poorly and degrading quickly during backtesting.
Pandemic risks, such as Covid-19, are difficult to insure as they are characterized by multiple factor risks and losses and involve different types of businesses and people simultaneously. The scarcity of time series and statistical data prevents insurers from developing correct pricing. We propose a model of catastrophe risk with Non-Damage Business Interruption (NDBI) policies to manage the pandemic risk due to the spread of Covid-19. The model employs a Monte Carlo simulation of different lockdown scenarios: the frequency and severity distributions of losses of Italian SMEs. The main results show the importance of a Covid-19 lockdown exposure NDBI policy, which benefits not only SMEs but also the insurer.
Economic theory. Demography, Business records management
Nilna Muna, Gede Crisna Wijaya, Putu Yuli Widya Astuti
The COVID-19 crisis is sweeping the world and affecting our lives, including consumer purchasing habits, preferences, and decisions. The survey, involving 251 respondents from Bali, Indonesia, examined how the pandemic affected Bali's purchasing decisions and consumer preferences. This paper aims to explore the impact of social media on consumer purchasing decisions, more specifically, examining the influence of social media on online shopping during the COVID-19 outbreak rooted in the service dominant logic (SDL) theory. Four hypotheses were developed and tested in the framework of a sample of 251 respondents from the Bali province. The proposed hypothesis was tested using AMOS 23 to perform structural equation modeling. The findings of this study suggest the critical role of social media interactivity in the two folds. First, parasocial interactions proved to be mediators of social media interactivity in improving purchasing decisions. Second, parasocial interaction has the potential to influence the decision-making of product purchasing preferences.
Roger Knecktys, Henrik Bette, Rüdiger Kiesel
et al.
Technology trends as digitalization and Industry 4.0 initiate a growing demand for new business models. Most of this models requires a fundamental shift of operational and financial risks between seller and buyer. A key question is therefore how to include additional risk pricing and hedging. In this paper we propose a new approach for a risk theory of innovative performance based business models as "Pay-for-Performance" or "Product as a Service". A new model and calculation method for determination the risk premium is presented. It contains beside financial price fluctuations also operational failure behaviour of products. We apply the model for a typical industrial application and simulate the pricing dependency for different cost distributions.
Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.
Purpose This study aims to investigate records management in selected government schools in South Africa. Design/methodology/approach The study adopted a qualitative research approach to collect in-depth data from the principals or administrators working as records managers at the school level. In all, a sample of principals and administrators from seven schools were interviewed face-to-face to enable the researcher to visit the schools in person. Findings The findings indicate that although the principals or administrators make concerted efforts to manage school records, there is no uniform system in place for this purpose. This results in haphazard record keeping and records management at the schools. The study recommends that the Department of Basic Education in Limpopo develop a file plan or model for schools to use to manage records uniformly in a standardised manner that would support accountability, transparency and good governance. Research limitations/implications The study was limited to seven primary schools in Lebopo Circuit, Limpopo province and secondary schools were excluded. Practical implications The author obtained a thorough understanding and direct insight into how principals or administrators manage school records. This study makes recommendations for the effective management of records in government schools. Originality/value This is one of the few research papers on records management in government schools in South Africa.
The study investigated the effect of psychological empowerment (PE) in between the High Performance Work System (HPWS) and Service Employees Performance (SEP) in the Automotive Retail Industry of Malaysia. We developed and examined a mediation model linking perceived-HPWS to SEP and PE plays as a mediator. From the self-determination theory, defined motivation as being intrinsic and extrinsic, whether PE enables the employees meeting the performance expectation. The proposed model can be substantiated wide data collection and data analysis. The research data are collected from a sample of 72 employees through a questionnaire. The findings suggest that the PE is a significant influence on SEP. It is expected that the findings of this study will guide Human Resource (HR) Practitioners to establish appropriate policies and procedures for effective HR practices to be deployed in the organization especially in the service industry environment.
Every business or start-up was e-ray by the government official ranging from local, state and federal level in Nigeria. Therefore, for SMEs to operate it’s a perquisite to follow the due process from registration by ministry of commerce and industry at state level and the various agencies responsible for registration. This challenge represents both widespread personal misfortune for individuals and a lost opportunity for critical national and global economic development. This study examines the challenges that affect the growth and development of small and medium enterprises in Bauchi state, Nigeria .The study was guided by an epistemological research philosophy adopting a positivist research paradigm. The research design was cross-sectional survey design using both quantitative and qualitative approaches. The target population for this study was 754 registered small and medium scale owned enterprises registered with the Bauchi state government by December 2019 and the sample size was 172 respondents derived using Fisher’s formulae. This study used a self-administered, closed, and open-ended questionnaire to obtain quantitative data. The study used descriptive statistics such as mean, standard deviation, median and proportions using the Statistical Package for Social Sciences (SPSS) version 24 and Microsoft Excel. Regression analysis and correlation analysis were used to determine the direction and strength of the relationship between the independent and the dependent variables.. The questionnaires were administered among the seventy (70) management staff members of the one hundred and seventy two (172) selected SMEs in Bauchi state Nigeria through purposive sampling method, and this was done personally by the researcher. Descriptive and inferential statistics was use to analyze and interpret the data use in this research. The study reveals that government policies is a major challenges that help SMEs to grow and achieve its stated objectives in global competitive environment. On same vain this study is so keen and motivated to established evidence on these challenges ascertain especially, in a context of current phenomena SMEs face in Bauchi state. Finally, the study will utilize the element of contingency theory, as these SMEs require sound institutional framework to mitigate these challenges. However, anecdotal evidence shows that SMEs in developing countries like Nigeria are faced with a lot of challenges that have hampered their growth in recent times. Among these challenges are: intermittent power supply, indiscriminate tax levies government policy, low accessibility to loans from financial institutions and inability to keep proper financial records. The study reveals that Government policies have a significant impact on the growth and development of SMEs in Bauchi State, Nigeria. Subsequently, the study recommends that management of SMEs should adopt good strategy in order to overcome these challenges.
Sociology (General), Economic history and conditions
The purchase intention is one of the stages buying decisions and shows the consumer's behavior to purchase a particular brand. The consumer’s purchase intention comes from attitudes toward the advertising and brand attitudes. For this reason, today, companies are trying to use celebrity endorsement advertisers to positively influence the attitudes toward the advertising and brand attitudes and subsequently increase purchase intention. The main purpose of this research is to explain the impact of celebrity endorsement on the consumer's purchase intention by mediating attitudes toward the advertising and brand attitudes. Therefore, in this research, the impact of celebrity endorsement on the consumer's purchase intention through the mediators of the attitude toward advertising and brand attitudes has been investigated.This study is an operational research in term of the purpose and in term of the type of collecting data is a descriptive-survey as a correlation type. The statistical population of the study consisted of male clear shampoo users in Bushehr. The sample size was estimated at 388 people. In this regard non- random available sample was used. Moreover, structural equation modeling was used to analyze the obtained data via smart PLS software. The findings of this research show that the use of celebrity endorsement in advertising has a positive and significant effect on the attitude toward advertising and brand attitudes. Also, the attitude toward advertising and brand attitudes has a positive and significant effect on the purchasing intention. Also, the celebrity endorser has a positive and significant effect on the purchasing intention through attitude toward advertising and brand attitudes. Therefore, companies must try to create a favorable attitude towards advertising and brand in the minds of consumers in order to increase the purchasing intention.