Hasil untuk "Business communication. Including business report writing, business correspondence"

Alice M. Obenchain-Leeson, Carole Ann Creque, Brian Satterlee et al.

Y. Fan, Peng Jiang, Milan Hemzal et al.

COVID-19 has been sweeping the world. The overall number of infected persons has been increased from 5 M in March 2020 to over 22 M in August 2020 and growing, which seems not to get its peak at the current stage. This has contributed to waste generation and different phases of challenges in waste management practices. The impacts including change in waste amount, composition, timing/frequency (temporal), distribution (spatial) and risk, which affects the handling and treatment practices. Recent impacts, challenges and developments on waste management in the response of COVID-19 have been assessed in this update. Singapore, the cities of Shanghai in China and Brno in the Czech Republic (a member state of the European Union), representing different pandemic development situation and also various cultural attitudes, are specifically analysed and discussed with current data. However, it should be noted that it is still fast developing. A varying trend in term of the waste amount is identified. Shanghai is showing a ~23% decline in household waste amount; however, Singapore is showing a ~3% increase, and Brno is showing a ~1% increase in household waste amount but ~40% decline in business and industrial waste. Manual sorting and recycling have been reported as restricted due to safety precaution. This is supported by the interview communication with ZEVO SAKO (the largest incineration plant in the Czech Republic). This study highlighted that the practices or measures at each place could serve as a guideline and reference. However, adaption is required according to the geographical and socioeconomic factors.

Ali Nour Eldin, Benjamin Dalmas, Walid Gaaloul

In recent years, there has been a growing interest in the verification of business process models. Despite their lack of formal characterization, these models are widely adopted in both industry and academia. To address this issue, formalizing the execution semantics of business process modeling languages is essential. Since data and process are two facets of the same coin, and data are critical elements in the execution of process models, this work introduces Proving an eXecutable BPMN injected with data, BPMN-ProX. BPMN-ProX is a low-code testing framework that significantly enhances the verification of data-aware BPMN. This low-code platform helps bridge the gap between non-technical experts and professionals by proposing a tool that integrates advanced data handling and employs a robust verification mechanism through state-of-the-art model checkers. This innovative approach combines theoretical verification with practical modeling, fostering more agile, reliable, and user-centric business process management.

Francesco Vinci, Gyunam Park, Wil van der Aalst et al.

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.

Andrea Zugarini, Andrew Zamai, Marco Ernandes et al.

Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.

Rui Zhu, Quanzhou Hu, Wenxin Li et al.

Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.

Sheetal.V. Hukkeri

. Corporate Social Responsibility (CSR), or as we commonly refer to it, is expected to dominate business reporting. Every company is required to have CSR policies and report on their related activities annually. This enables us to describe both socially responsible practices and socially irresponsible activities, which can be recognized. Today, CSR is considered an advanced universal concept that has evolved and grown methodologically. It is a globally known language and perspective that has become increasingly significant. In this decade, partners are expected to prioritize more than just making money and complying with the law; they must also demonstrate concern for business development. CSR has become an integral part of modern-day business. Social Impact: CSR studies enable us to comprehend how businesses affect society and what they have to offer. They examine how businesses promote sustainable practices, address social and environmental challenges, and enhance local communities. Research sheds light on the positive impact that corporations can have on society through the analysis of CSR programs and their outcomes. Stakeholder Involvement: CSR research emphasizes the importance of participation, including that of employees, clients, suppliers, communities, and investors. It investigates how businesses interact with and respond to their stakeholders to promote cooperation, communication, and trust. By developing deeper relationships and understanding successful stakeholder engagement tactics, businesses can enhance their social license to operate. Sustainability: CSR research plays a crucial role in the development of sustainable business operations. It explores how companies incorporate resource efficiency, environmental considerations, and climate change mitigation measures into their day-to-day practices. Research helps generate best practices and facilitates the transition to a more sustainable economy by identifying effective sustainability initiatives. Gray correlation analysis is a tool originally proposed by Deng to address MCTM (Multiple Criteria and Targeted Measure) problems. It has been successfully used to resolve various MCTM problems. GRA (Gray Relational Analysis) stands for the analysis pattern that examines the serial and data type relationship or geometric pattern between measurable impacts in a communication evaluation model. The following factors were analyzed in the study: Community relations, Diversity aspects, Employee relations, Ecological environment, Product aspects, Ownership by family, Ownership by founder, Ownership by mutual funds, Ownership by banks and insurance firms, Ownership by employees (ESOP), Family CEO (dummy), Founder CEO (dummy), Debt/equity, Return on assets. From the results, it is seen that the Founder CEO (dummy) has obtained the first rank, whereas the ownership by banks and insurance firms has the lowest rank.

Rania Mousa, Peterson K. Ozili

The rapid use and development of information and communication technology capabilities in the public sector has revolutionized the mechanism that government agencies use to collect, process, and disseminate data. Electronic government is one of the strategic initiatives that many government agencies have considered adopting to offer efficient web-based services and operations. Although there have been efforts to examine the implementation process of technological innovations in financial and business reporting, many government agencies are about to face a bigger challenge in developing or adopting current technologies to assess their usefulness for non-financial sustainability reporting. The Extensible Business Reporting Language, XBRL, has been adopted by the U.S. Federal Deposit Insurance Corporation (FDIC) to process financial data in the quarterly call reports filed by banks. Using Rogers’ well-established theory of innovation adoption process, this paper discusses the FDIC’s XBRL implementation process and investigates the roles and experiences of the agency’s stakeholders. A case study research methodology, supported by semi-structured interviews, is used to explore each phase of the implementation process. The findings reveal that the process was facilitated by stakeholder engagement, technical support, and the agency’s strategic decision-making process. This paper contributes to the literature by examining the applications, benefits, and challenges of using XBRL technology to process non-financial sustainability data, which is still an under-researched area. Therefore, the implications for using the technology in non-financial reporting will be insightful for future regulatory adopters and their stakeholders including filer banks, software vendors, and various users of financial and non-financial information.

Xiangdan Piao, Jun Xie, Shunsuke Managi

Background This study provides objective evidence on the impact of COVID-19 based on employee occupational stress reported from 13 different industries, and examines the determinants of employee psychological well-being. As the economic and social impacts of the COVID-19 pandemic continue, governments should consider industry-level differences when making support decisions concerning public resource allocation to corporations. However, little evidence exists regarding the differences in occupational stress across industries. Methods Employee occupational stress data ( N  = 673,071) was derived from workers in Japan from 2018 to 2020. The sample comprises workers from 13 industries, including civil services, service industry (other), real estate, medical/welfare, wholesale/retail, academic research, and accommodation/restaurant business. A logit model is employed to investigate the differences in employees’ psychological well-being before and during the pandemic. Results In 2020, 11 out of 12 industries had significantly worse occupational stress compared to employees engaged in civil services. Over 23% of employees from the wholesale/retail and accommodation/restaurant industries were observed as high-stress employees. Improved compensation policies supporting these industries are suggested. In contrast, reduced occupational stress was found among employees in the transportation/postal and information/communication industries. Among the 13 industries, aside from high job demands, tough inter-person relationships in the workplace became the most significant stressors during the pandemic. Conclusions The results confirm that the pandemic has had a heterogeneous effect on employee occupational stress across industries, thus suggesting that the level of compensation given to different industries during the COVID-19 pandemic should be discussed and approved by the Japanese government. Additionally, support for the wholesale/retail and accommodation/restaurant industries during the pandemic should be improved.

Simona Munzarova, J. Košťálová, Eliška Fialová

This paper focuses on the issue of non-financial corporate reporting by the Czech chemical companies. Based on the content analysis of the websites and disclosures, it analyses, compares and evaluates the level of web communication of the economic, environmental and social issues of corporate social responsibility of these companies. At first, it presents results of the research comparing the extent of non-financial web communication of selected Czech chemical companies with the Czech leaders in the Czech TOP 100 rankings. These results are then complemented by results of the quantitative research on environmental instruments used by all Czech chemical production companies, that use websites for communication of their activities. The number of companies in the Czech Republic processing non-financial reports is significantly lower than abroad, but it is still growing. Large enterprises pay more attention to non-financial reporting. Rather than including this information in a disclosure, a business website is used as a tool for this communication. The paper brings sectoral perspective into non-financial reporting literature, as attention is paid to the chemical production sector and also complements the knowledge of communication of companies that do not belong to the group of 100 most important companies within the country.

Pavel V. Shevchenko, Jiwook Jang, Matteo Malavasi et al.

In this study we examine the nature of losses from cyber related events across different risk categories and business sectors. Using a leading industry dataset of cyber events, we evaluate the relationship between the frequency and severity of individual cyber-related events and the number of affected records. We find that the frequency of reported cyber related events has substantially increased between 2008 and 2016. Furthermore, the frequency and severity of losses depend on the business sector and type of cyber threat: the most significant cyber loss event categories, by number of events, were related to data breaches and the unauthorized disclosure of data, while cyber extortion, phishing, spoofing and other social engineering practices showed substantial growth rates. Interestingly, we do not find a distinct pattern between the frequency of events, the loss severity, and the number of affected records as often alluded to in the literature. We also analyse the severity distribution of cyber related events across all risk categories and business sectors. This analysis reveals that cyber risks are heavy-tailed, i.e., cyber risk events have a higher probability to produce extreme losses than events whose severity follows an exponential distribution. Furthermore, we find that the frequency and severity of cyber related losses exhibits a very dynamic and time varying nature.

Marlon Dumas, Fabiana Fournier, Lior Limonad et al.

AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.

Gatot Soepriyanto, Sienny Tjokroaminoto, Arfian Zudana

Purpose This study aims to examine the association between annual report readability and accounting irregularities in Indonesia. Using 967 firm-year observations over the 2014–2017 period, this paper unable to find evidence that annual report readability is associated with accounting irregularities. The results are robust after using alternate measurements of accounting irregularities proxies and readability indexes. This paper also finds that the corporate governance mechanism and foreign shareholder structure did not moderate the association between annual report readability and accounting irregularities. Design/methodology/approach The study uses an archival method with cross-sectional regression of 967 firm-year observations over the 2014–2017 period to investigate an association between annual report readability and accounting irregularities in an emerging market setting. To check the robustness of the results, this paper conducts a battery of robustness tests. Findings This paper finds evidence that annual report readability is not associated with accounting irregularities in Indonesia. The results are robust after using alternate measurements of accounting irregularities proxies and readability indexes. This paper also finds that the corporate governance mechanism and foreign shareholder structure did not moderate the association between annual report readability and accounting irregularities. This implies that the readability of annual reports does not have the ability to predict the likelihood of accounting irregularities in Indonesia. It is possible that firms with accounting irregularities will be inclined to voice simpler stories which can counteract the tendency of lies to be linguistically more complex. Indeed, according to the Education First English Proficiency Index, Indonesia is categorized at a low proficiency level. Furthermore, this paper also discovers that the average readability of the management discussion and analysis (MD&A) of Indonesian public listed firms is at an ideal score by having a Fog Index of 13.32. The findings provide valuable insights for stakeholders in using annual reports for their decision-making, especially in an emerging market setting and non-English speaking countries. Research limitations/implications It is important to interpret the findings in the context of the limitations of the readability index the authors used. It is argued that Fog Index, Flesch-Kincaid and Flesch Reading Ease have their own limitations as considered inadequate to be used in the context of business and accounting narratives that are adult-oriented and specialist in nature (Jones and Shoemaker, 1994; Loughran and McDonald, 2014). Another caveat relates to the use of proxies for accounting irregularities. The M-Score and F-Score have some limitations in which, among others, were determined without considering the normal level of accruals or period where manipulations were absent (Ball, 2013). Practical implications One reason underlying the result is that Indonesian firms, in general, do not consider the complexity of the annual report, particularly MD&A disclosures, as a tool to mask financial reporting irregularities. It is also possible that firms with accounting irregularities will incline to voice simpler stories because it is difficult to be untruthful (Lo et al., 2017). Indeed, according to Education First English Proficiency Index, Indonesia was categorized in low proficiency level and ranked 61st out of 100 countries being surveyed (Education First, 2019). As policymakers, locally and globally, are calling for more simplified reports including a plain English approach, the study can be insightful to their deliberations. It suggests that policymakers need to consider a country’s English proficiency, writing skills, regulatory environment and corporate policy on shaping the complexity and narrative of a firm’s communications. Originality/value The study contributes to a scarcity of research that investigates English-written annual reports in non-English speaking countries (Jeanjean et al., 2015; Lundholm et al., 2014). As such, the study findings provide insights related to MD&A in an under-researched area and contribute to improving MD&A not only in Indonesia but also in neighbor countries that share similar social, political and economic characteristics. Also, this study is important for foreign institutions or individuals investing on Indonesian-listed firms. According to Candra (2016), approximately 60% of companies listed in the Indonesia stock exchange are owned by foreign individuals or institutions. They rely greatly on the English texts of annual reports to understand the companies’ financial performance. Moreover, La Porta et al. (2002) asserted that firms with a majority of foreign shareholders (dominantly owned by foreign investors) are more likely to face information asymmetry, primarily due to geographical factors and language barriers.

Anthony Townley

This article reports on the use of discourse maps in conjunction with genre and discourse analysis to help teach communicative practices for contract negotiation. Using one map as a baseline to understand the intertextual process of negotiating a contract in communication with business clients and counterpart lawyers, other maps can zoom in and examine the discursive features of email genres, cover letters, and different versions of the contract under negotiation. The type of discourse maps developed in this study can be utilized for task-based writing materials and role-play activities that facilitate the authentic experience of negotiating a business deal.

Dr. Robert Martínez-Carrasco

Chun Ouyang, Renuka Sindhgatta, Catarina Moreira

Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models. With the development of interpretable machine learning techniques, explanations can be generated for a black-box model, making it possible for (human) users to access the reasoning behind machine learned predictions. In this paper, we aim to present an approach that allows us to use model explanations to investigate certain reasoning applied by machine learned predictions and detect potential issues with the underlying methods thus enhancing trust in business process prediction models. A novel contribution of our approach is the proposal of model inspection that leverages both the explanations generated by interpretable machine learning mechanisms and the contextual or domain knowledge extracted from event logs that record historical process execution. Findings drawn from this work are expected to serve as a key input to developing model reliability metrics and evaluation in the context of business process predictions.

Erika Janeth Salazar Jiménez , Carlos Eduardo Castaño Ríos , Julián Esteban Zamarra Londoño

In the local and Latin American context, MSMEs are considered an important productive and economic engine, and they represent between 95% and 98% of the total population of the region’s companies. This research aims to characterize the accounting and financial management practices of MSMEs in eastern Antioquia, in Colombia. To do so, a sample of 379 MSMEs in this region was taken and a questionnaire of 42 questions was applied to them about the use, the type of information they prepare and other characteristics related to these financial information disclosure practices. The results obtained show that in most cases the information prepared by these companies meets the minimum presentation requirements; however, their characteristics and uses do not exceed the intention of legal compliance towards a real and conscious management decision making process.

Xue Han, Lianxue Hu, Yabin Dang et al.

Automatic process discovery from textual process documentations is highly desirable to reduce time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the structural relationships between activities, which is important in the whole process discovery scope, is still a challenge. In fact, a business process has latent semantic hierarchical structure which defines different levels of detail to reflect the complex business logic. Recent findings in neural machine learning area show that the meaningful linguistic structure can be induced by joint language modeling and structure learning. Inspired by these findings, we propose to retrieve the latent hierarchical structure present in the textual business process documents by building a neural network that leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with process-level language model objective. We tested the proposed approach on data set of Process Description Documents (PDD) from our practical Robotic Process Automation (RPA) projects. Preliminary experiments showed promising results.

Efrén Rama-Maneiro, Juan C. Vidal, Manuel Lama

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

Peter Schrammel

Most businesses rely on a significant stack of software to perform their daily operations. This software is business-critical as defects in this software have major impacts on revenue and customer satisfaction. The primary means for verification of this software is testing. We conducted an extensive analysis of Java software packages to evaluate their unit-testability. The results show that code in software repositories is typically split into portions of very trivial code, non-trivial code that is unit-testable, and code that cannot be unit-tested easily. This brings up interesting considerations regarding the use of test coverage metrics and design for testability, which is crucial for testing efficiency and effectiveness. Lack of unit-testability is an obstacle to applying tools that perform automated verification and test generation. These tools cannot make up for poor testability of the code and have a hard time in succeeding or are not even applicable without first improving the design of the software system.