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

Ruiming Min

This study examines whether the tariff policies delivered on promises to revitalize American manufacturing and create jobs. Using county-level business application data from 2018-2025, we analyze the relationship between tariff implementation and new business formation through linear regression analysis. Our findings reveal a statistically significant positive association between US tariffs on China and American business applications. However, when Chinese retaliatory tariffs are included in the analysis, their negative coefficient substantially exceeds the positive US tariff effect, suggesting that retaliatory measures largely offset the benefits of protectionist policies. Control variables including inflation rate, federal funds rate, and government spending show significant positive effects on business formation. These results indicate that while protectionist trade policies may stimulate domestic business formation, their effectiveness is significantly diminished by retaliatory responses from trading partners. The study provides evidence that unilateral tariff measures without diplomatic coordination produce limited net benefits, confirming that trade wars create scenarios where potential gains are neutralized by counteractions.

Elizabeth Ankrah, Stephanie Nyairo, Mercy Muchai et al.

Small and medium sized businesses often struggle with data driven decision making do to a lack of advanced analytics tools, especially in African countries where they make up a majority of the workforce. Though many tools exist they are not designed to fit into the ways of working of SMB workers who are mobile first, have limited time to learn new workflows, and for whom social and business are tightly coupled. To address this, the Dukawalla prototype was created. This intelligent assistant bridges the gap between raw business data, and actionable insights by leveraging voice interaction and the power of generative AI. Dukawalla provides an intuitive way for business owners to interact with their data, aiding in informed decision making. This paper examines Dukawalla's deployment across SMBs in Nairobi, focusing on their experiences using this voice based assistant to streamline data collection and provide business insights

Chiara Polli, Carmen Serena Santonocito

In the field of online crisis management communication, AI-based social media listening tools (i.e., tools designed to track and monitor online conversations about a topic or brand) play a pivotal role in opinion mining and reputation audits. Compared to manual analyses, AI enables a faster large-scale collection and classification of vast amounts of data from several online platforms, thus facilitating the task of detecting and monitoring the sentiment linked to a brand and/or product. Nonetheless, AI-based analyses are far from unbiased. This paper adopts a critical multimodal perspective to explore the challenges linked to sentiment analysis in digital multimodal aggregations. It presents an empirical study in which the results of sentiment analysis performed by the social media listening tool Meltwater and manual tagging are compared to evaluate the efficacy of the tool in assessing online reputation damage following a crisis event. The findings of this study suggest that, in addition to possible incorrect classifications of texts (e.g. lack of understanding of pragmatic features or texts in languages other than English), the AI-based tool’s misinterpretation of emotional cues also includes multimodal ensembles. This is due to social media listening tools’ dependence on unimodal (verbal-only) classifiers that fail to produce reliable outputs. Despite the predictive power of these tools, the findings ultimately indicate that the accuracy of sentiment analysis is still affected by a hard-to-die bias concerning the primacy of language over non-verbal communication – a trend which is in contrast with the multimodal nature of semiosis and the proliferation of complex multimodal artifacts online.

Nora Hoffmann, Fridrun Freise

Although writing centers based on the US model are now widespread at German universities, they are still in the process of being established and institutionalized. This article (Roles and Possibilities of Influence of Writing Centers at German Universities. First Results of a Germany-wide Survey) explores the question of what role writing centers currently play at German universities after the expiry of the Quality Pact for Teaching, which facilitated their rise, and how they are institutionally positioned as actors. The article presents the initial results of a survey conducted in spring 2023 among writing centers at German universities, in which 77 of 146 existing centers participated. Data is presented on the institutional placement of the centers, the range of services offered, target groups, advice provided to university management, research activities, participation in internal decisions, and staffing. The aim of this article is to provide an empirically based stocktaking of the status of writing centers in Germany.

Bo Li

Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-export-growth areas. Most importantly, our unique research design enables us to perform the most comprehensive tests on theories (hypotheses) regarding the business cycle. We show that the following theories (hypotheses) cannot explain the cause of the 1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real business cycle theory, the collateral-driven credit cycle theory, the business uncertainty theory, and the extrapolative expectation theory.

Suresh Kamath

The development of an IT strategy and ensuring that it is the best possible one for business is a key problem many organizations face. This problem is that of linking business architecture to IT architecture in general and application architecture specifically. In our earlier work we proposed Category theory as the formal language to unify the business and IT worlds with the ability to represent the concepts and relations between the two in a unified way. We used rCOS as the underlying model for the specification of interfaces, contracts, and components. The concept of pseudo-category was then utilized to represent the business and application architecture specifications and the relationships contained within. The linkages between them now can be established using the matching of the business component contracts with the application component contracts. However the matching was based on manual process and in this paper we extend the work by considering automated component matching process. The ground work for a tool to support the matching process is laid out in this paper.

Alistair P. Barros, Arthur H. M. ter Hofstede, Henderik A. Proper

The workflow concept, proliferated through the recently emergent computer supported cooperative work (CSCW) systems and workflow systems, advances information systems (IS) implementation models by incorporating aspects of collaboration and coordination in business processes. Under traditional implementation models, applications are partitioned into discrete units of functionality, with (typically) operational procedures used to describe how human and computerised actions of business processes combine to deliver business services. In this paper, a number of essential modelling concepts and features for business transaction workflows are developed.

Giacomo Bergami, Chiara Di Francescomarino, Chiara Ghidini et al.

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing event logs stored by the systems supporting the execution of a business process. In this paper, the problem of explaining deviations in business processes is first investigated by using features based on sequential and declarative patterns, and a combination of them. Then, the explanations are further improved by leveraging the data attributes of events and traces in event logs through features based on pure data attribute values and data-aware declarative rules. The explanations characterizing the deviances are then extracted by direct and indirect methods for rule induction. Using real-life logs from multiple domains, a range of feature types and different forms of decision rules are evaluated in terms of their ability to accurately discriminate between non-deviant and deviant executions of a process as well as in terms of understandability of the final outcome returned to the users.

Rodrigo Rebouças de Almeida

Technical debt happens when teams take shortcuts on software development to gain short-term benefits at the cost of making future changes more expensive. Previous results show that there is a misalignment between the prioritization done by technical professionals and the prioritization expected by business ones. This paper presents a business-driven approach to prioritize technical debt items. The research is organized into four phases: exploratory, to identify the research focus; concept verification, where the proposed approach was evaluated on a multi-case study; solution, where a design science research was conducted to develop Tracy, a framework for technical debt prioritization; and validation. Results so far show that the business-driven prioritization of technical debt items can improve the alignment and communication between the technical and business stakeholders.

Haodong Bai, Frank Z. Xing, Erik Cambria et al.

Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate classification for small companies with innovative business models. Second, emerging markets are fast-developing, thus the static business taxonomies cannot promptly reflect the new features. In this article, we propose a new method to construct business taxonomies automatically from the content of corporate annual reports. Extracted concepts are hierarchically clustered using greedy affinity propagation. Our method requires less supervision and is able to discover new terms. Experiments and evaluation on the Chinese National Equities Exchange and Quotations (NEEQ) market show several advantages of the business taxonomy we build. Our results provide an effective tool for understanding and investing in the new growth companies.

Albero, Alejandra

In the present economic context which fosters economic globalization, the internationalization of the big financial institutions is a key factor to increase the integration of the different banking systems worldwide. For this reason, it is important to study which variables, both internal and external, influence the internationalization process. This paper describes the expansion strategy of the Spanish bank BBVA, where three stages are identified. The outcomes of this process is analysed, as well. The study shows how factors such as productivity and financial deregulation are determinant is the strategy’s selection. Furthermore, the diversification stage reached makes it possible to obtain new benefit sources and reduce the impact of financial crisis in both national and international markets.

Elisabet Tiselius

Sign language interpreting of dialogues shares many features with the interpreting of dialogues between non-signed languages. We argue that from a cognitive perspective in dialogue interpreting, despite some differences between the two types of interpreting, sign language interpreters use many of the same processes and handle similar challenges as interpreters between non-signed languages. We report on a first exploration of process differences in sign language interpreting between three novice and three experienced Swedish Sign Language interpreters. The informants all interpreted the same dialogue and made a retrospection of their interpreting immediately after the task. Retrospections were analyzed using tools for identifying reported processing problems, instances of monitoring, and strategy use (see Ivanova 1999). Furthermore, the interpreting products (both into Swedish Sign Language and into Swedish) and their differences were qualitatively analyzed. The results indicate that there are differences between the two groups, both in terms of the retrospective reports and in terms of the interpreting product. As expected, monitoring seems to be a factor determined by experience. The experienced interpreters seemed to have more efficient ways of handling turn taking and the internalization of new vocabulary. The study also concludes that to use instruments devised for simultaneous conference interpreting (Ivanova 1999; Tiselius 2013), the instruments need to be adapted to the dialogue setting, even though in the case of sign language interpreting the simultaneous interpreting technique is used even in dialogue interpreting.

Jean Paul Sarrazin

This article presents an updated revision of the different definitions of the concept of “religion” in the social sciences. The objective of this revision was to find an analytical category that is precise, clear and sufficiently broad to empirically study the wide range of sociocultural phenomena that can be or have been considered “religious”, even if they are not necessarily found in organized communities, or in institutionalized religions. However, the results of this revision show that the definitions are numerous, divergent, and many of them are ambiguous and imprecise, which is why any attempt at systematization is hindered and the analytical distinction between religious and other types of sociocultural phenomena is problematized. Without pretending to argue in favor of one of the definitions, the need to carefully use any of them is pointed out here, since each definition entails cultural specificities, biases and limitations that highlight certain aspects of reality, while hiding others. It is concluded that the lack of clarity and consensus on the subject can generate confusion and important misunderstandings in the academic community, which impedes the construction of scientific knowledge. Despite the absence of a unified analytical category, it is observed that some of the most prominent elements in the various definitions can constitute, in themselves, useful analytical categories for empirical research

Vijay K. Bhatia

No abstract.

John Humbley

La terminographie se réduit-elle à la lexicographie spécialisée? L’expérience que les pays francophones ont faite de la terminologie donne à penser qu’il s’agit plutôt de deux méthodologies proches, qui puisent en partie, mais non exclusivement, dans les mêmes sources, et qui ont des finalités qui ne sont pas nécessairement identiques. Une part importante de la distinction serait d’ordre culturel, et nous proposons une explication personnelle de cette spécificité, qui, en France comme au Québec, lie la terminologie, et donc la terminographie, à la politique linguistique.

Maissa Tamraz, Yaming Yang

This contribution is concerned with price optimisation of the new business for a non-life product. Due to high competition in the insurance market, non-life insurers are interested in increasing their conversion rates on new business based on some profit level. In this respect, we consider the competition in the market to model the probability of accepting an offer for a specific customer. We study two optimisation problems relevant for the insurer and present some algorithmic solutions for both continuous and discrete case. Finally, we provide some applications to a motor insurance dataset.

Remco Dijkman, Geoffrey van IJzendoorn, Oktay Turetken et al.

Business process models describe the way of working in an organization. Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow. However, they often present an idealized view. This means that unexpected exceptions - exceptions that are not modelled in the business process model - can also occur in practice. This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions. This paper studies the relation between the occurrence of exceptions and operational performance. It does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional. Subsequently, it analyzes the differences between normal and exceptional paths. The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions.

Carmen Daniela Maier

No abstract.

Luciano García-Bañuelos, Alexander Ponomarev, Marlon Dumas et al.

Blockchain technology enables the execution of collaborative business processes involving untrusted parties without requiring a central authority. Specifically, a process model comprising tasks performed by multiple parties can be coordinated via smart contracts operating on the blockchain. The consensus mechanism governing the blockchain thereby guarantees that the process model is followed by each party. However, the cost required for blockchain use is highly dependent on the volume of data recorded and the frequency of data updates by smart contracts. This paper proposes an optimized method for executing business processes on top of commodity blockchain technology. The paper presents a method for compiling a process model into a smart contract that encodes the preconditions for executing each task in the process using a space-optimized data structure. The method is empirically compared to a previously proposed baseline by replaying execution logs, including one from a real-life business process, and measuring resource consumption.

Andrea Burattin, Vered Bernstein, Manuel Neurauter et al.

Business process models abstract complex business processes by representing them as graphical models. Their layout, solely determined by the modeler, affects their understandability. To support the construction of understandable models it would be beneficial to systematically study this effect. However, this requires a basic set of measurable key visual features, depicting the layout properties that are meaningful to the human user. The aim of this research is thus twofold. First, to empirically identify key visual features of business process models which are perceived as meaningful to the user. Second, to show how such features can be quantified into computational metrics, which are applicable to business process models. We focus on one particular feature, consistency of flow direction, and show the challenges that arise when transforming it into a precise metric. We propose three different metrics addressing these challenges, each following a different view of flow consistency. We then report the results of an empirical evaluation, which indicates which metric is more effective in predicting the human perception of this feature. Moreover, two other automatic evaluations describing the performance and the computational capabilities of our metrics are reported as well.