Hasil untuk "Business records management"

R. Fairlie

Abstract Social‐distancing restrictions and health‐ and economic‐driven demand shifts from COVID‐19 are expected to shutter many small businesses and entrepreneurial ventures, but there is very little early evidence on impacts. This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from the April 2020 Current Population Survey—the first month fully capturing early effects. The number of active business owners in the United States plummeted by 3.3 million or 22% over the crucial 2‐month window from February to April 2020. The drop in active business owners was the largest on record, and losses to business activity were felt across nearly all industries. African‐American businesses were hit especially hard experiencing a 41% drop in business activity. Latinx business owner activity fell by 32%, and Asian business owner activity dropped by 26%. Simulations indicate that industry compositions partly placed these groups at a higher risk of business activity losses. Immigrant business owners experienced substantial losses in business activity of 36%. Female business owners were also disproportionately affected (25% drop in business activity). Continuing the analysis in May and June, the number of active business owners remained low—down by 15% and 8%, respectively. The continued losses in May and June, and partial rebounds from April were felt across all demographic groups and most industries. These findings of early‐stage losses to small business activity have important implications for policy, income losses, and future economic inequality.

D. McKenzie, C. Woodruff

Galiya Klinkova, Michael Grabinski

Business cycles (a periodic change of e.g. GDP over five to ten years) exist, but a proper explanation for it is still lacking. Here we extend the well-known NAIRU (non-accelerating inflation rate of unemployment) model, resulting in a set of differ-ential equations. However, the solution is marginal stable. Therefore we find a nat-ural sinusoidal oscillation of inflation and unemployment just as observed in busi-ness cycles. When speculation is present, the instability becomes more severe. So we present for the first time a mathematical explanation for business cycles. The steering of central banks by setting interest rates to keep inflation stable and low needs an overhaul. One has to distinguish between real monetary instability and the one caused naturally by business cycles.

Jorge Cisneros, Timothy Wojan, Matthew Williams et al.

Public-use microdata samples (PUMS) from the United States (US) Census Bureau on individuals have been available for decades. However, large increases in computing power and the greater availability of Big Data have dramatically increased the probability of re-identifying anonymized data, potentially violating the pledge of confidentiality given to survey respondents. Data science tools can be used to produce synthetic data that preserve critical moments of the empirical data but do not contain the records of any existing individual respondent or business. Developing public-use firm data from surveys presents unique challenges different from demographic data, because there is a lack of anonymity and certain industries can be easily identified in each geographic area. This paper briefly describes a machine learning model used to construct a synthetic PUMS based on the Annual Business Survey (ABS) and discusses various quality metrics. Although the ABS PUMS is currently being refined and results are confidential, we present two synthetic PUMS developed for the 2007 Survey of Business Owners, similar to the ABS business data. Econometric replication of a high impact analysis published in Small Business Economics demonstrates the verisimilitude of the synthetic data to the true data and motivates discussion of possible ABS use cases.

Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake et al.

This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework is to optimize the use of the conversation history by dynamically managing context for maximizing the relevant information provided to a ConvQA model within its token limit. Our approach incorporates a Context Manager (CM) Module, a Summarization (SM) Module, and an Entity Extraction (EE) Module in a bid to handle the conversation history efficaciously. The CM Module dynamically adjusts the context size, thereby preserving the most relevant and recent information within a model's token limit. The SM Module summarizes the older parts of the conversation history via a sliding window. When the summarization window exceeds its limit, the EE Module identifies and retains key entities from the oldest conversation turns. Experimental results demonstrate the effectiveness of our envisaged framework in generating accurate and contextually appropriate responses, thereby highlighting the potential of the ACM framework to enhance the robustness and scalability of the ConvQA systems.

Ahmed Adekunle

Economic theory posits that government debt, when done at reasonable rates, can foster economic growth, particularly in developing countries. Nonetheless, the heavy burden of government debt faced by many emerging nations poses challenges in fulfilling financial commitments and can impede progress. As a sequel to this, this study evaluates the relationship between control of corruption, public debt and economic growth in South Africa. Data are obtained from the World Development Indicators (WDI, 2022), the study employed the Autoregressive Distributed Lag method (ARDL) to analyse the data. The findings of this study show that public debt significantly hinders economic growth per capita. According to the study, economic growth is unlikely to benefit from the unsustainable buildup of public debt unless corruption is reduced through control of corruption. Therefore, it is recommended to lower government debt by funding suitable infrastructure and preventing corruption to boost economic growth in their country. Since economists have maintained that economic growth is the most effective means of reducing poverty, economic expansion will lower unemployment and poverty on the African continent.

Wahyu Firmandani, Izmi Dwira Eriani, Rizka Miladiah Ervianty et al.

Background: MSMEs in Gresik Regency, particularly members of the Gresik Business Pioneer Community (KPUG), face challenges due to the continued use of manual financial records and non-environmentally friendly packaging, which limit access to financing and contribute to increased plastic waste. Objective: This community service initiative aimed to enhance the managerial capacity of MSMEs through financial digitalization and to promote the use of eco-friendly packaging as part of a green marketing strategy toward a smart and sustainable city. Method: The program was implemented in five stages: (1) socialization to raise awareness, (2) technical training based on partner needs, (3) application of technology through the use of automatic cash registers and biodegradable packaging, (4) continuous mentoring, and (5) evaluation using pre-tests, post-tests, and satisfaction surveys. Results: The program significantly improved participants’ competencies in digital financial management and the adoption of eco-friendly packaging. The average post-test score was 94, with all participants scoring above 80. Participant satisfaction averaged 4 out of 5. Conclusion: The activity effectively strengthened MSMEs’ managerial and marketing capacities, enhanced their access to financing opportunities, and supported circular economy practices aligned with environmental sustainability.

Sanyukta Chhibber, Babita Rawat, S. Tyagi et al.

Blockchain technology has the potential to revolutionize HRM procedures, but its impact on hiring, onboarding, and performance reviews remains unknown. This study uses a quantitative research approach, involving an employee, organizational leader, and HR professional survey, to quantify the influence of blockchain on HR productivity, assess its protection of confidential HR information, and improve personnel management transparency. Confirmatory factor analysis (CFA) and structural equation modelling (SEM) are used to examine data from businesses that have integrated blockchain technology into their HRM systems. The results show that blockchain effectively protects HR data, ensures privacy, and enhances transparency in employee records, certifications, and career advancement tracking. Transparency is the second most important factor correlated with blockchain adoption, after privacy. These findings provide valuable advice for businesses considering or navigating the use of blockchain technology in HR administration. Future research should focus on off-boarding procedures, such as updating employment records, managing payroll, and conducting exit interviews.

Emmanuel Jeffrey Dzage, G. Szabados

The significant role of corporate social responsibility (CSR) in achieving sustainability and in meeting the expectations of stakeholders has been well documented. Using a collection of 2173 publications on CSR and its connections with business performance, this study conducted a bibliometric investigation using the Systematic Literature Network Analysis (SLNA) technique combined with network visualizations to demonstrate the current research trends, most topical themes and the developing areas of interest in the growing field of CSR and its linkages with business performance for an approximate period from 2004 to 2023 as published in the Scopus database of two decades. The goal was to explore the research gaps by analyzing the most cited authors and most impactful publications by year, location, subject area and document type. The study also outlined the trends by topic prevalence, commonly used keywords and citation networks based on co-occurrence and co-authorship to identify the current thematic gaps. The results reveal a mild rate of growth in scholarly interest around the field of CSR and business performance until 2022, where a manifold increase in publications was recorded. An expanding focus on human, social and organizational behavior, economic systems, financial and social performance, leadership, stakeholder management and management science was identified, although there is a scarcity of studies around issues regarding developing countries, climate change, CSR disclosure and small businesses. These findings demonstrate the current state of the research and offer interesting insights and timely research directions as a roadmap for future studies.

Herbert Endres, J. Auburger, Roland Helm

In the rapidly evolving landscape of industrial innovation, a major hurdle for business customers is the inherent uncertainty associated with adopting new products. This uncertainty is often exacerbated by the digital transformation, which contributes to an overwhelming influx of information. Central to this challenge is the in-adequacy of knowledge transfer from salespeople to business customers, leading to a suboptimal understanding of the new offerings and consequently, a reluctance to adopt these innovations. Despite its significance, the strategies to effectively mitigate this issue have remained largely unexplored. Our study addresses this gap by examining the impact of salespeople's selling capabilities on the adoption of knowledge by business customers. Selling capability, defined as the capability of individuals to perform salespeople's tasks, emerges as a critical factor in facilitating customers' understanding and acceptance of new industrial innovations. We conducted comprehensive surveys with business customers focusing on their experiences with recent incremental industrial innovations, complemented by objective purchase data from company records. This research is pioneering in empirically establishing that the adoption of knowledge by customers acts as a mediating factor between salespeople's selling capabilities and the purchase of innovations. Intriguingly, our findings reveal the existence of an optimal level of selling capability necessary for effective knowledge transfer, which varies depending on specific contingencies. This discovery is crucial for sales, innovation, and marketing managers, suggesting that relying solely on selling capabilities might be insufficient. We recommend the integration of additional strategies, such as assertive listening, to enhance knowledge transfer. Such strategies can prevent the pitfalls of overreliance on selling capabilities alone and foster a more effective adoption of industrial innovations among business customers. Our findings offer valuable insights for professionals aiming to navigate the complexities of selling industrial innovations in the digital age, providing a nuanced understanding of how to tailor their approach to improve customer receptivity and adoption rates.

Meilin Zhao, Xiaohong Wang, Lei Cheng

This study investigates the influence of business strategy on green total factor productivity (GTFP), focusing on the mediating effect of digital transformation and the moderating effect of organizational legitimacy. Leveraging micro-data from China’s listed industrial enterprises spanning from 2011 to 2021, findings reveal: (1) Prospector strategies exhibit a greater propensity to enhance GTFP compared to defender strategies. (2) Digital transformation partially mediates between business strategy and GTFP. (3) Organizational legitimacy moderates between business strategy and GTFP. Specifically, when a company possesses robust financial standing (pragmatic legitimacy), a better environmental track record (moral legitimacy), and establishment of Environmental Management System certification (cognitive legitimacy), the positive relationship between business strategy and GTFP is strengthened. (4) Further research indicates that business strategy exerts a more pronounced effect on enhancing GTFP for companies with larger scales and weaker political connections, as well as those situated in regions characterized by high fiscal revenue and favorable legal environments.

Fawad Ullah, Lei Shen, Syed Hamad Hassan Shah

Abstract purpose Value co-creation (VCC) recently displayed a significant increase in the frequency of publications in business studies and social sciences. Our study objects to explore the current state of VCC research in the business-to-business (B2B) context, principally in the marketing field. Research design, approach, and methodology This research article extracted research papers on VCC in the B2B context published in the last two decades through the Web of Science (WoS). Initially, we applied HistCite to determine the research dynamics of VCC articles and then VOS viewer to conduct bibliographic coupling and cartographic analysis. Furthermore, we found the most co-occurred keywords in the abstracts, titles, and keywords. Findings Our research explored that the United Kingdom was the most important country with 27 publications and 594 citations. Aarikka-Stenroos L was the most influential author, among his research is a systematic review which revealed that scholars of B2B journals adopted the term business “ecosystem” and studied the implications of ecosystem perspective in business and innovation networks and received the most citations. Industrial Marketing Management (IMM) was the most influential journal because it published 8 of the 10 most cited articles. One hundred and six out of 121 publications were in Business research and seventy-six were in management area, which made it the most hot and critical research area. Lappeenranta University was the most essential organization in VCC research based on the most records published and second-highest citations. Research limitations/implications and future research Four research streams have emerged which indicate the prominent role of VCC in the B2B context (1) VCC and relationships, (2) VCC and organizational capabilities, (3) VCC and actors’ engagement at various platforms, and (4) VCC and processes. Our research paper provided a base for conceptualizing publications related to business, management, operations research management science, and social sciences interdisciplinary on VCC in the B2B context. Content analysis has revealed that research work on VCC in the B2B context is at an early stage in the marketing arena. Along with bringing some sort of consensus regarding researchers’ opinion toward the nature and modality of VCC literature and process in the B2B context, we urge future research to focus on how relationships and their precursors can be efficiently utilized to co-create and enhance value within B2B interactions. We also request future research to focus on making the VCC process sustainable and viable both on a time and economical basis. Practical implications Organizations can involve customers and producers to work jointly to co-create value for their goods and services with negligible cost to achieve higher market shares and a competitive edge over rivals. Originality/value This might be the first bibliometric study conducted on VCC in the B2B context (there are some Bibliometric VCC publications, but they are not B2B-specific, our research is the first Bibliometric study conducted on VCC in the B2B context) in the marketing field and can expose novel avenues for future research.

Ziyou Gong, Xianwen Fang, Ping Wu

Event log records all events that occur during the execution of business processes, so detecting and correcting anomalies in event log can provide reliable guarantee for subsequent process analysis. The previous works mainly include next event prediction based methods and autoencoder-based methods. These methods cannot accurately and efficiently detect anomalies and correct anomalies at the same time, and they all rely on the set threshold to detect anomalies. To solve these problems, we propose a business process anomaly correction method based on Transformer autoencoder. By using self-attention mechanism and autoencoder structure, it can efficiently process event sequences of arbitrary length, and can directly output corrected business process instances, so that it can adapt to various scenarios. At the same time, the anomaly detection is transformed into a classification problem by means of selfsupervised learning, so that there is no need to set a specific threshold in anomaly detection. The experimental results on several real-life event logs show that the proposed method is superior to the previous methods in terms of anomaly detection accuracy and anomaly correction results while ensuring high running efficiency.

Sreyoshi Bhaduri, Satya Kapoor, Alex Gil et al.

Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent

Weizhen Bian, Siyan Liu, Yubo Zhou et al.

Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001

Milad Ghanbari, Zohreh Dehdashti Shahrokh

Objective: In this study, the main purpose of this study is to investigate the effect of gaimification on consumer loyalty with respect to the mediating role of attitude towards the brand and intention to engage.Methodology: In terms of data collection method, this research is a descriptive survey and is applied in terms of purpose. The statistical population of the present study, Iranian customers and buyers across the country, is the online store of Digikala. Sampling method is available sampling. Online tool, the main tool for data collection. On the other hand, Cronbach's alpha coefficient and combined reliability were used to evaluate the reliability of the research. The Sobel test is used to measure the significance of the effect of the mediating variable. The research data are also analyzed in the form of structural equation modeling based on the partial least squares method using Amos software.Findings:This model, which included the direct effect of gaimification on consumer loyalty as well as the effect of gaimification through mediating attitude towards the brand and the intention of engagement on consumer loyalty, its components were tested. Also, in evaluating the structural part of the model, there is a strong relationship between endogenous and exogenous structures and the general research model has a good fit.Conclusion:After analyzing the data, it was found that gaimification(Perceived usefulness, perceived ease of use, perceived social impact, perceived pleasure) through mediating variable attitude towards the brand and intention to engage, directly and indirectly have a positive and significant effect on consumer loyalty.

Muhammadsuhaimee Yanya, Thitima Theppaya, Domiree Esa

The production of Kapiyoh hats in the southern border areas of Thailand is a business that is aligned with the community’s way of life and culture, and it helps generate income through domestic sales and exports abroad. However, there is now price competition because operators who do not know the real cost of production. As a result, many entrepreneurs have been forced to shut down their businesses. Most entrepreneurs do not use information technology to help them manage their businesses, so they do not keep various records in the form of databases. This affects the information used to calculate the cost of manufacturing the product, product pricing, and accounting, resulting in information that is inconsistent with reality. The objectives of this research were to develop an information system for the Kapiyoh Hat business management by entrepreneurs in southern border provinces and to assess the information system usage satisfaction. The information system was developed by using Bootstrap Framework as a development tool that was implemented in accordance with the SDLC system development process. The process consisted of procedures for defining problems and collecting requirements, system analysis, design, development, testing, implementation, and maintenance. This facilitates an information system that supports the work of the entrepreneur in every work process from start to finish by saving various items of information in the database. This database consists of information about raw materials, product sales, labor costs, and other expense information. Entrepreneurs are able to search and display the results of the data and accounts according to the time period. According to the survey of 19 entrepreneurs, using the information system, the level of satisfaction with the information system design was at a high level and the usage of the information system menu was also at a high level.

Preetam Basu, Palash Deb, Ashutosh Kumar Singh

Purpose Businesses must now track the complicated supply chains of their products, which involve different manufacturers and suppliers. However, because supply chains are scattered across multiple countries and involve many institutions, it becomes an overwhelming practical challenge to ensure transparent recording and reporting of greenhouse gas emissions. The myriad issues necessitate a technological solution that will improve supply chain transparency, assist in managing carbon assets and allow all parties to obtain credible information on carbon output. As a potential solution, this study offers a unique architecture that effectively combines “blockchain technology” with the carbon supply chain of a multi-institution business network. Design/methodology/approach This research and proposed framework are based on publicly available reports on carbon emissions tracking, sustainability, carbon trade and emerging blockchain technologies. The authors also interviewed industry experts to obtain their input and feedback. Findings Businesses must support the pledges made by their respective governments towards meeting the objectives of the Paris Agreement. Although the emissions trading system encourages businesses to move in this direction, it can be challenging for them to efficiently manage their carbon assets owing to issues such as lack of standardised methods for tracking emissions across suppliers and manufacturers and the fragmentation of carbon markets. The carbon supply chain can maintain a record of the chronological flow of carbon emissions and eventually of all carbon assets by integrating a centralised ledger system based on blockchain technology. Originality/value Global warming, climate change and carbon emissions are among humanity’s pressing problems today. To achieve net zero emissions by the middle of the 21st century, emissions must be drastically reduced. Global supply chains have a crucial role to play in this context. This article provides a blockchain-based technology framework for carbon emissions visibility and tracking. The authors believe such a platform will provide critical visibility and tracking support to globally dispersed supply chains, moving a step closer towards carbon emissions control and net zero operations.

Charlotte Hesselbarth, S. Schaltegger

Haiyue Yuan, Matthew Boakes, Xiao Ma et al.

Commercial organisations are holding and processing an ever-increasing amount of personal data. Policies and laws are continually changing to require these companies to be more transparent regarding the collection, storage, processing and sharing of this data. This paper reports our work of taking Booking.com as a case study to visualise personal data flows extracted from their privacy policy. By showcasing how the company shares its consumers' personal data, we raise questions and extend discussions on the challenges and limitations of using privacy policies to inform online users about the true scale and the landscape of personal data flows. This case study can inform us about future research on more data flow-oriented privacy policy analysis and on the construction of a more comprehensive ontology on personal data flows in complicated business ecosystems.