Hasil untuk "Business"

F. Reichheld

Curtis P. Armstrong, V. Sambamurthy

Kartikey Singh Bhandari, Tanish Jain, Archit Agrawal et al.

Customer reviews contain rich signals about product weaknesses and unmet user needs, yet existing analytic methods rarely move beyond descriptive tasks such as sentiment analysis or aspect extraction. While large language models (LLMs) can generate free-form suggestions, their outputs often lack accuracy and depth of reasoning. In this paper, we present a multi-agent, LLM-based framework for prescriptive decision support, which transforms large scale review corpora into actionable business advice. The framework integrates four components: clustering to select representative reviews, generation of advices, iterative evaluation, and feasibility based ranking. This design couples corpus distillation with feedback driven advice refinement to produce outputs that are specific, actionable, and practical. Experiments across three service domains and multiple model families show that our framework consistently outperform single model baselines on actionability, specificity, and non-redundancy, with medium sized models approaching the performance of large model frameworks.

Muhammad Zohaib, Muhammad Azeem Akbar, Sami Hyrynsalmi et al.

The emergence of agentic AI systems in 6G software businesses presents both strategic opportunities and significant challenges. While such systems promise increased autonomy, scalability, and intelligent decision-making across distributed environments, their adoption raises concerns regarding technical immaturity, integration complexity, organizational readiness, and performance-cost trade-offs. In this study, we conducted a preliminary thematic mapping to identify factors influencing the adoption of agentic software within the context of 6G. Drawing on a multivocal literature review and targeted scanning, we identified 29 motivators and 27 demotivators, which were further categorized into five high-level themes in each group. This thematic mapping offers a structured overview of the enabling and inhibiting forces shaping organizational readiness for agentic transformation. Positioned as a feasibility assessment, the study represents an early phase of a broader research initiative aimed at developing and validating a layered maturity model grounded in CMMI model with the software architectural three dimensions possibly Data, Business Logic, and Presentation. Ultimately, this work seeks to provide a practical framework to help software-driven organizations assess, structure, and advance their agent-first capabilities in alignment with the demands of 6G.

Eugenie Y. Lai, Yeye He, Surajit Chaudhuri

Business Intelligence (BI) plays a critical role in empowering modern enterprises to make informed data-driven decisions, and has grown into a billion-dollar business. Self-service BI tools like Power BI and Tableau have democratized the ``dashboarding'' phase of BI, by offering user-friendly, drag-and-drop interfaces that are tailored to non-technical enterprise users. However, despite these advances, we observe that the ``data preparation'' phase of BI continues to be a key pain point for BI users today. In this work, we systematically study around 2K real BI projects harvested from public sources, focusing on the data-preparation phase of the BI workflows. We observe that users often have to program both (1) data transformation steps and (2) table joins steps, before their raw data can be ready for dashboarding and analysis. A careful study of the BI workflows reveals that transformation and join steps are often intertwined in the same BI project, such that considering both holistically is crucial to accurately predict these steps. Leveraging this observation, we develop an Auto-Prep system to holistically predict transformations and joins, using a principled graph-based algorithm inspired by Steiner-tree, with provable quality guarantees. Extensive evaluations using real BI projects suggest that Auto-Prep can correctly predict over 70\% transformation and join steps, significantly more accurate than existing algorithms as well as language-models such as GPT-4.

Jiaming Wang, Zhe Tang, Zehao Jin et al.

As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), and the evaluation of LLM capabilities in such contexts has not been fully explored. To bridge this gap, we propose SOP-Maze, a benchmark constructed from real-world business data and adapted into a collection of 397 instances and 3422 subtasks from 23 complex SOP scenarios. We further categorize SOP tasks into two broad classes: Lateral Root System (LRS), representing wide-option tasks that demand precise selection; and Heart Root System (HRS), which emphasizes deep logical reasoning with complex branches. Extensive experiments reveal that nearly all state-of-the-art models struggle with SOP-Maze. We conduct a comprehensive analysis and identify three key error categories: (i) route blindness: difficulty following procedures; (ii) conversational fragility: inability to handle real dialogue nuances; and (iii) calculation errors: mistakes in time or arithmetic reasoning under complex contexts. The systematic study explores LLM performance across SOP tasks that challenge both breadth and depth, offering new insights for improving model capabilities. We have open-sourced our work on: https://github.com/meituan-longcat/SOP-Maze.

Maksym Avramenko, David Chapela-Campa, Marlon Dumas et al.

Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary use-case is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and shortfalls in available resources. An approach to tackle this use-case is to run a long-term simulation up to a point where the workload is similar to the current one (warm-up), and measure performance thereon. However, this approach does not consider the current state of ongoing cases and resources in the process. This paper studies an alternative approach that initializes the simulation from a representation of the current state derived from an event log of ongoing cases. The paper addresses two challenges in operationalizing this approach: (1) Given a simulation model, what information is needed so that a simulation run can start from the current state of cases and resources? (2) How can the current state of a process be derived from an event log? The resulting short-term simulation approach is embodied in a simulation engine that takes as input a simulation model and a log of ongoing cases, and simulates cases for a given time horizon. An experimental evaluation shows that this approach yields more accurate short-term performance forecasts than long-term simulations with warm-up period, particularly in the presence of concept drift or bursty performance patterns.

Nimol Thuon, Jun Du

Automated document layout analysis remains a major challenge for low-resource, non-Latin scripts. Khmer is a language spoken daily by over 17 million people in Cambodia, receiving little attention in the development of document AI tools. The lack of dedicated resources is particularly acute for business documents, which are critical for both public administration and private enterprise. To address this gap, we present \textbf{KH-FUNSD}, the first publicly available, hierarchically annotated dataset for Khmer form document understanding, including receipts, invoices, and quotations. Our annotation framework features a three-level design: (1) region detection that divides each document into core zones such as header, form field, and footer; (2) FUNSD-style annotation that distinguishes questions, answers, headers, and other key entities, together with their relationships; and (3) fine-grained classification that assigns specific semantic roles, such as field labels, values, headers, footers, and symbols. This multi-level approach supports both comprehensive layout analysis and precise information extraction. We benchmark several leading models, providing the first set of baseline results for Khmer business documents, and discuss the distinct challenges posed by non-Latin, low-resource scripts. The KH-FUNSD dataset and documentation will be available at URL.

Tumelo Rasedile

Entrepreneurship education has become increasingly important worldwide, including for graduates in South Africa. Like their counterparts in other emerging economies, South African graduates face challenges in starting their businesses. This study focused on Graphic Design graduates at the Tshwane University of Technology (TUT), who encountered difficulties in establishing their design businesses. The research aimed to develop an entrepreneurship education framework for the Integrated Communication Design (ICD) programme at Tshwane University of Technology. A qualitative method was used along with an interpretivism research philosophy to examine small and micrographic design businesses in Tshwane’s central business district. In this article, twelve participants were chosen through purposive sampling. Four main themes were revealed in this article namely, core entrepreneurial skills, curriculum challenges in practical education, challenges faced by graphic design graduates, and strategies for improving business performance. These findings informed the proposed framework for entrepreneurial education for Tshwane University of Technology. The study identified ten key challenges for graphic design graduates, such as a lack of contracts, mentorship, and financial support, difficulties in hiring skilled staff, limited business knowledge, reliance on government contracts, excessive subcontracting, brand recognition issues, cash flow problems, and corruption. The study concluded by emphasising the urgent need for a shift in graphic design education, particularly in South Africa. It is recommended that graphic design educational institutions and the government join hands in finding ways to sponsor these entrepreneurs financially and non-financially to make their transition into the corporate world as smooth as possible.

David Oluseun Olayungbo, Aziza Zhuparova, Mamdouh Abdulaziz Saleh Al-Faryan et al.

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Tahseen Anwer Arshi, Joseph Wallis

Objective: The objective of the article is to provide an entrepreneurial value-based perspective that can either drive or derail circular economy (CE) adoption and related strategies. The study argued that fundamental shifts toward CE adoption require a more profound value-based change. Research Design & Methods: Existing studies have analysed several self-transcending values in advancing circular economy (CE). However, an adequate investigation is yet to occur on self-advancing values that can obstruct CE adoption and practice in an entrepreneurial context. Embedded within a norm activation model (NAM) and informed by value-belief-norm theory (VBN), the study builds on cross-lagged data (n=477) to explain the clash between dominant self-advancing entrepreneurial values and CE strategies. Findings: The SEM-based machine-learning test results predicted that entrepreneurial hedonic and egoistic values complemented by hedonic and egoistic consumption reciprocally drive linearity rather than circularity within entrepreneurship. However, awareness of the consequences of adverse CE business models on society and the environment moderates the effect of self-enhancing values on CE strategies. Implications & Recommendations: Policy instruments and macro-level societal intervention in creating, enhancing, and balancing self-transcendence values with self-advancing values can improve CE adoption across the entrepreneurial architecture. Contribution & Value Added: The study is one of the first to demonstrate entrepreneurial value-oriented barriers to circularity, derailing CE diffusion to the broader entrepreneurial landscape. It suggests measures to enhance CE adoption among entrepreneurs.                    

Kiran Busch, Alexander Rochlitzer, Diana Sola et al.

GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.

Emanuel Kohlscheen, Richhild Moessner, Daniel Rees

We test the international applicability of Friedman s famous plucking theory of the business cycle in 12 advanced economies between 1970 and 2021. We find that in countries where labour markets are flexible (Australia, Canada, United Kingdom and United States), unemployment rates typically return to pre-recession levels, in line with Friedman s theory. Elsewhere, unemployment rates are less cyclical. Output recoveries differ less across countries, but more across episodes: on average, half of the decline in GDP during a recession persists. In terms of sectors, declines in manufacturing are typically fully reversed. In contrast, construction-driven recessions, which are often associated with bursting property price bubbles, tend to be persistent.

Andreas Metzger, Tristan Kley, Aristide Rothweiler et al.

Prescriptive business process monitoring provides decision support to process managers on when and how to adapt an ongoing business process to prevent or mitigate an undesired process outcome. We focus on the problem of automatically reconciling the trade-off between prediction accuracy and prediction earliness in determining when to adapt. Adaptations should happen sufficiently early to provide enough lead time for the adaptation to become effective. However, earlier predictions are typically less accurate than later predictions. This means that acting on less accurate predictions may lead to unnecessary adaptations or missed adaptations. Different approaches were presented in the literature to reconcile the trade-off between prediction accuracy and earliness. So far, these approaches were compared with different baselines, and evaluated using different data sets or even confidential data sets. This limits the comparability and replicability of the approaches and makes it difficult to choose a concrete approach in practice. We perform a comparative evaluation of the main alternative approaches for reconciling the trade-off between prediction accuracy and earliness. Using four public real-world event log data sets and two types of prediction models, we assess and compare the cost savings of these approaches. The experimental results indicate which criteria affect the effectiveness of an approach and help us state initial recommendations for the selection of a concrete approach in practice.

Yoni Nazarathy, Zbigniew Palmowski

We study critical GI/G/1 queues under finite second moment assumptions. We show that the busy period distribution is regularly varying with index half. We also review previously known M/G/1/ and M/M/1 derivations, yielding exact asymptotics as well as a similar derivation for GI/M/1. The busy period asymptotics determine the growth rate of moments of the renewal process counting busy cycles. We further use this to demonstrate a BRAVO phenomenon (Balancing Reduces Asymptotic Variance of Outputs) for the work-output process (namely the busy-time). This yields new insight on the BRAVO effect. A second contribution of the paper is in settling previous conjectured results about GI/G/1 and GI/G/s BRAVO. Previously, infinite buffer BRAVO was generally only settled under fourth-moment assumptions together with an assumption about the tail of the busy-period. In the current paper we strengthen the previous results by reducing to assumptions to existence of $2+ε$ moments.

Charles I. Plosser

Farida Naili, Nuryakin

The relational capability can create networks and build relationships to be an essential part of a company to improve business performance. This study aims to empirically prove the influence of knowledge sharing on product innovation, the effect of network capability on product innovation and business performance, the effect of relational ability on product innovation and business performance, and the effect of product innovation on business performance. The sample of this research was created from owners of batik manufacturing SMEs in Lasem, Rembang, Central Java. The study used SEM-PLS for analysis. The results found that (1) knowledge sharing had a positive and significant effect on product innovation; (2) network capability had a positive and significant impact on product innovation and business performance; (3) relational ability had a positive and significant effect on product innovation and business performance; (4) greater effect of product innovation affects business performance. The role of product innovation is to mediate between knowledge sharing and marketing performance. SMEs can improve business performance.

Brayan Rodríguez, Christian Arroyo, Luis H. Reyes et al.

Important institutions, such as the World Health Organization, recommend reducing alcohol consumption by encouraging healthier drinking habits. This could be achieved, for example, by employing more effective promotion of non-alcoholic beverages. For such purposes, in this study, we assessed the role of experiential beer packaging sounds during the e-commerce experience of a non-alcoholic beer (NAB). Here, we designed two experiments. Experiment 1 evaluated the influence of different experiential beer packaging sounds on consumers’ general emotions and sensory expectations. Experiment 2 assessed how the sounds that evoked more positive results in Experiment 1 would influence emotions and sensory expectations related to a NAB digital image. The obtained results revealed that a beer bottle pouring sound helped suppress some of the negativity that is commonly associated with the experience of a NAB. Based on such findings, brands and organizations interested in more effectively promoting NAB may feel encouraged to involve beer packaging sounds as part of their virtual shopping environments.

Lei Zhao, Ying Wang

In the post-pandemic era. tourism industry in Hubei Province has entered a critical period of transformation in the face of post-pandemic development. Based on the matrix model of SWOT-PEST analysis method, in this paper, the strengths, weaknesses, opportunities and threats of tourism development in Hubei Province were analyzed, and reference basis and feasible suggestions were provided for the development of tourism industry in Hubei Province in the post-paiidemic era from the political, economic, social and technical aspects.

I Made Dwi Harmana

Penelitian ini bertujuan untuk mengetahui pengaruh pengalaman, idealisme, dan komitmen profesional pada pembuatan keputusan etis konsultan pajak. Penelitian ini menggunakan data primer yaitu berdasarkan jawaban responden atas kuisioner yang disebarkan pada 215 orang Konsultan Pajak terdaftar di Wilayah Bali-Nusa Tenggara. Teknik penentuan sampel menggunakan purposive sampling. Populasi dalam penelitian ini adalah seluruh anggota IKPI (Ikatan Konsultan Pajak Indonesia) Wilayah Bali-Nusa Tenggara sebanyak 215 orang dan berdasarkan kriteria yang ditentukan jumlah sampel sebanyak 100 orang. Teknik analisis data dilakukan dengan menggunakan teknik analisis regresi linier berganda. Hasil analisis menunjukkan bahwa pengalaman, idealisme, dan komitmen professional berpengaruh positif pada pembuatan keputusan etis konsultan pajak.