Actual Business Research Articles

The method to determine position in 3D digital sand table

This paper proposes a method to determine the working position in the 3D digital sand table. Starting from analyzing the actual business of the 3D digital sand table to propose the operation cases and build the algorithm to determine the position for each of those cases. Then, the presentation of the application of that method to specific functions of the GlobeDesktop system. Finally, the paper presents experimental content to evaluate the method.

Why Do Consumers Use Ride-Hailing? Evidence from China and Indonesia

Consumers’ attitudes and preferences in developing countries differ from those in developed countries. The research compared the motivation to use ride-hailing in developing and developed countries to understand consumers better. The research applied a qualitative survey with 52 drivers, followed by a quantitative survey with 741 passengers. It was finalized by one quantitative survey of 818 respondents to answer the research questions. The passengers in both quantitative surveys were from Indonesia and China who were Chinese, Indonesian, and Expatriates in China. Indicators in Partial Least Square-Structural Equation Modelling (PLS-SEM) were used to capture ride-hailing phenomena closer to actual business situations and made more robust measurements for future testing of consumers’ decisions in sharing economy setting. The results show that the habit factor influences the decision to use ride-hailing among Indonesian, Chinese, and Expatriate respondents. Meanwhile, the financial factor only influences Indonesian respondents. Safety factor affects Expatriate respondents, and utility and convenience factors influence the decision of Chinese respondents. Innovation and social factors are significant when the significance level is reduced to the exploratory level. In conclusion, managers of sharing economy companies in developing and developed countries can use Importance-Performance Mapping Analysis (IPMA) result in the research to identify important but weak factors that can be improved.

Readiness for Innovation of Emerging Grass-Based Businesses

New business opportunities based on grassland and green fodder present a promising avenue to realize the transition towards a circular and sustainable bio-based economy. Yet, such potential remains largely untapped and grass-based products and businesses remain a small niche in the global economy. To understand this phenomenon, this paper introduces and operationalizes a model to assess innovation readiness built around seven focus areas: technology, manufacturing, business, IPR, customer, team, and funding readiness with their own detailed “progress scales.” We employ necessary condition analysis (NCA) to identify limiting factors and bottlenecks in actual business situations. Our results reveal that lack of consumer awareness, infant conversion technologies and paucity of long-term investments that support emerging bio-based businesses are the most limiting conditions for the growth of emerging grass-based markets. The present study advances our understanding of the factors that limit complex innovations in grassland systems. Focusing on necessary conditions in a coordinated way between practitioners and policy makers by giving priority to fostering positive awareness of bioeconomy businesses, developing conversion technologies, and improving access to capital is a recommended approach to foster emerging grass-based innovations.

EBSF: Node Characteristics-Based Block Allocation Plans for Efficient Blockchain Storage

The heavy storage problem has become a key obstacle to the application of blockchain to the actual business environments, because each node needs to keep a complete replica of blockchain data. The data volume grows undesirably large in practice. It prevents the widely used devices, e.g., tablets and mobile phones, to join blockchain systems due to their limited storage and computing resource. Previous work addressed the storage issue by allowing participating nodes to only keep a fraction of the entire transaction set, e.g., sharding. However, existing studies focus on transaction placement with the minimum cross-shard communications. These studies neglect the node characteristics (e.g., storage capacity, cost, and response capability), which impacts the storage performance adversely. In this paper, we propose EBSF, a block storage framework that achieves efficient block storage by constructing a block allocation plan based on node characteristics. Blockchain nodes are organized into committees such that nodes in a committee work together to maintain the entire blockchain data. We formulate the block allocation plan problem that assigns each block to at least one node in a committee. The goal is to minimize the total cost while reaching the threshold of the response capability of each block. We prove the NP-hardness of the problem and propose heuristic algorithms. We also propose two strategies to handle the dynamic scenario of new blocks. Extensive evaluation shows the efficiency and effectiveness of the proposed framework.

Proactive contract theory in the context of corporate sustainability due diligence

The article discusses how proactive contract theory could be used to translate the ambitious objectives of the European Commission's current draft proposal on corporate sustainability due diligence into actual business practices in complex value chains and networks. Proactive contract theory regards contracts as legal, economic, managerial and social artefacts that should be designed to prevent undesirable results and promote favourable outcomes. Moreover, the theory highlights the need for multiprofessional collaboration and user-centricity in contract design. Based on these principles, we introduce four ways to promote corporate sustainability due diligence in supply contracts via proactive contracting: turning from one-sided safeguarding towards shared responsibility and collaboration, incentivizing responsible and sustainable business practices via promotive contract clauses, engaging end users in the contract design process and preventing disputes.

Gain more with less: Extracting information from business documents with small data

Information extraction (IE) is a vital step of digitization that reduces paperwork in offices. However, the adaptation of common IE systems to actual business cases faces two issues. First, the number of training samples is small (i.e. 100–200 examples). Second, span extraction models based on question answering formulation require a long time for training and inference. To overcome these issues, we introduce a new query-based model for the extraction of information from business documents. For data limitation, the model employs transfer learning which adapts the knowledge of pre-trained language models (i.e. BERT) to specific domains. To do that, we design a new CNN layer for the adaptation of the model to specific domains. For the speed, different from the encoding of normal span extraction methods (BERT-QA), the proposed model encodes short tags and context documents in two channels in parallel, which speeds up training and inference time. Information from short tags is fused with context documents learned from CNN by using attention to predict start and end positions of extracted spans. Promising results on five domain-specific datasets in English and Japanese indicate that the proposed model produces high-quality outputs and can be applied for business scenarios.

R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era

Nowadays, China’s food consumption structure is shifting from being survival-oriented to health-oriented. However, the food industry is still facing a research and development (R&D) dilemma. Scientific evaluation of an enterprise’s R&D performance can help to reduce the investment risk of R&D and promote economic benefits. This study implements the dynamic data envelopment analysis (DDEA) technique to measure and evaluate the level of R&D performance in the Chinese food manufacturing industry. Twenty-eight listed companies were selected for the study, considering the time period from 2019 to 2021. After constructing a system of inputs, outputs and carry-over indicators, overall and period efficiency scores were obtained. The results reveal that the overall level of R&D in the industry is relatively low (0.332). Average efficiency scores across years were estimated as 0.447, 0.460, 0.430 for 2019, 2020, and 2021, respectively. Lastly, this study considers the actual business situation of the industry and makes suggestions for improvement from the perspective of enterprises and the government; these anticipate aiding the food manufacturing industry to improve the performance management of R&D activities.

Editorial Policy and Style Information

Editorial Policy and Style Information

Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance.

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.

The Case of Geoffrey Chaucer and Cecily Chaumpaigne: New Evidence

The Case of Geoffrey Chaucer and Cecily Chaumpaigne: New Evidence

Clustering Production and Operational Practices of SME Using Correspondence Analysis: A Case Study of Northern OTOP Thailand

This study aims to identify the actual conditions of the current operational practices of the One Tambon One Product (OTOP) and the actual sales and income. This research summarizes and classifies the existing practices concerning different groups of OTOP products and presents empirical evidence on how and what practices correspond to the actual business performance. This research adopted correspondence analysis as a data analysis and visualization tool to summarize and present if clusters or relationships exist among categorical variables of OTOP groups, operation practices and their revenue performances. The results can then be used to define the right set of operations on the OTOP group for the Thai government to develop a supporting policy.

A SOFTWARE SOLUTION FOR QUALITY ASSESSMENT OF BUSINESS PROCESS MODELS

One of the primary tools of the BPM (Business Process Management) paradigm is business process modeling. Business scenarios can be presented in the form of graphical models that can be easily understood by both Information Technology (IT) professionals and non-IT professionals – business analysts, software customers, department heads, top managers, and other stakeholders interested in business process improvement – using business process modeling. Small and large businesses can capture their operations in the form of graphic diagrams using business process modeling as the fundamental tool of the BPM approach, which can then be brainstormed by business analysts to uncover ways to optimize organizational workflows. Typically, business process improvement is accomplished by the automation of operations that have been recognized as “bottlenecks” following analysis. However, analyzing a business process model is only viable if it is clear and correct in terms of compliance with both the notation used and the actual business process it reflects. Therefore, this work examines the structural measures of the BPMN (Business Process Model and Notation) business process model. It is assumed that business process models that violate business process modeling rules are neither understandable nor suitable for further work with them, which can also lead to various errors occurring during the stage of business process analysis, as well as the stage of its improvement and implementation of proposed changes, i.e., during development, testing, and maintenance of distinct software components, information system modules, and so on. The object of this work is a process of quality assessment of business process models created using the BPMN notation. The subject of this work is a software solution for the quality assessment of business process models. The goal of this work is the evaluation of a probability of errors in business process models to improve their quality.

Business Brand Research Based on Multi-Feature Fusion Emotion Analysis.

With the deepening of globalization, brand plays an important role in determining the competitiveness of enterprises. It is worth thinking about how to quantify the brand value reasonably to achieve the purpose of improving the competitiveness of enterprises. The research of commercial brands based on emotion analysis extracts the views of consumers on the evaluation data of brand attributes, analyzes the emotional tendency of consumers' views, and then helps enterprises adjust their production strategies. The purpose of emotion analysis is to judge users' views and attitudes toward goods or services by analyzing texts. In this paper, the linear support vector machine model is used to extract and study the features in sentiment analysis. Then, the brand based on the fusion model is evaluated, and the experimental conclusions are drawn: the method of fusing the depth features extracted by word vector and the named entity extracted by CRF makes the brand effect of fusing emotional factors better than the feature extraction method only using CRF named entity recognition. It is proved that the model method proposed in this paper has a certain role in actual business. Feature fusion often combines shallow model methods and fuses various shallow feature screening technologies based on word segmentation results. This paper introduces fusion features and supplements feature vectors based on the results of the deep learning word vector model. Support Vector Machine (SVM) maps feature vectors to some sample points in the space, finds a plane that can realize sample classification, and maximizes the distance between the two sample points closest to the plane in two types of samples, to maximize the classification performance and improve the generalization of the model.

Strategic Management of External Disruptions on Realization of Business Plans—Case of Serbian Manufacturing Companies

This research paper analyzes the key influences of external disruptions on the business sustainability of medium-sized manufacturing companies operating in Serbia. This paper explores the strategic management of massive external disruptions (such as the global pandemic in 2020) and unveils key correlations between internal and external influences. The quantitative part of this research includes two phases; the first was conducted in 2019, with predictions made in the pre-pandemic period, and the second phase was conducted in 2022 by comparing the predictions with actual business results. Existing theoretical research has already identified many weaknesses, including how strategic business outlooks (in relation to economic downturns and bottlenecks within supply chains) influence the success of medium-sized companies, as well as internal factors, such as strategic management and the flexible adjustment of business plans. Taking into account that the whole world is still recovering after the pandemic, the empirical research conducted on 580 Serbian companies sheds light on how the pandemic disruption has affected businesses. As business plans relate to forecasted revenue, profit margins and investment effects, the main results of this study confirm that predicted business results are significantly influenced by supply chain disruptions, negatively affecting companies relying on the import of raw material and companies that have widely internationalized their sales efforts.

Thinking and Countermeasures for The Delivery of Maritime Police Forces Upon the Prediction of Large-scale Mass Incidents in The Confrontation Between Chinese and Philippine Fishermen at Sea

In order to solve the practical problems of the marine police force in the South China Sea, combined with the actual business in the field of transportation and investment, this paper analyzes the problems and causes of the existing support mode, comprehensively considers them, and points out several aspects and practical measures that need to be paid attention to in order to plan in advance and provide reference and help for the force to solve practical problems.

Spatial variations in financial constraints of SMEs—evidence from firm-level estimates of investment-cash flow sensitivities in Sweden

It is well established that there is uneven availability of credit across space, in particular for SMEs. The evidence on whether this translates into differences in actual business investments remains scarce. We assess this question by using firm-level data for Swedish firms and estimate the extent to which the average investment-cash flow sensitivities of firms vary across the urban–rural hierarchy. We find that the world of financing is not yet flat for the majority of Swedish SMEs. Companies located in non-metro regions are most dependent on own cash flow in their investments. The results hold for all firms, firms of different sizes, firms operating in low-end services, unaffiliated firms and those belonging to domestic corporations. In contrast, investment-cash flow sensitivity of firms operating in high-tech services and those belonging to a multinational enterprise does not differ geographically. On average, regional investment-cash flow sensitivity is lower in bigger, denser and more educated local labour market regions; it is higher in regions with greater concentration of SMEs.

Application of BP Neural Network in Matching Algorithm of Network E-Commerce Platform

In order to solve the matching algorithm problem of network e-commerce platform, a method of applying BP neural network in the network e-commerce platform matching algorithm is proposed. First of all, combined with the actual situation of the platform, select 9 factors that are most in line with the company’s actual business model to influence the selection for analysis; secondly, import 60 sets of data into MATLAB software, measure the input and output data uniformly, and divide the sample data matrix into training set and test. Finally, after multiple factor combinations and verifications, it is concluded that in the training model of the five main factors, the prediction results of the model are compared with the real values. The feasibility of establishing the selection model based on BP neural network is proved. Online e-commerce platforms can refer to this model to build a product selection model that meets the needs of the platform, helping enterprises to achieve more efficient product selection work. Since the parameter initialization of the neural network is random, although the output results are different after the program runs for many times, the R2 is still stable between 0.7 and 1.0, which proves that the predicted value made by the system is highly approximate to the real value and can achieve the predicted effect.

Firm innovation and covenant tightness

This study explores the association between firm innovation and loan covenant strictness. We find that lenders construct stricter contracts for firms filing more patents, consistent with lenders imposing more oversight on firms when they enter the commercialization stage after having demonstrated their inventiveness. Our results hold under propensity score matching and entropy balancing, and when exploiting the American Inventors Protection Act as a shock affecting unrelated banks’ access to patent filing information. The relationship we document is stronger when the lender has more expertise and for firms with higher default risk. We demonstrate that borrowers’ patent filings are associated with more future R&D and capital investment and with a higher likelihood of their acquiring firms in the industry of their patent filings. Our results are consistent with the theoretical prediction that lenders interpret patent filings as indicative of high inventive potential that requires stricter discipline and oversight by lenders in order to be converted into actual business success, and with them designing debt contract terms accordingly.

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Editorial Policy and Style Information

Aging Small Business Owner-managers and Business Exit: Longitudinal Investigation

The context of this study is business exit decisions of aging small business owner-managers. Because of the intention-action gap that exists between the theoretical modeling and actual behavior, and the existence of multiple important goals (e.g. business exit, retirement), we do not fully grasp the social psychological mechanism underlying actual business exit among aging small business owner-managers. In our theorizing efforts, we argue that aging small business owner-managers' perceived control over a target behaviour (i.e. business exit), combined with higher levels of general self-efficacy, is likely to facilitate business exit intention turning into actual business exit. We collected empirical, longitudinal data among aging Finnish small business owner-managers to test a mediated moderated model. Our main empirical findings suggests that attitude-behavioural models (e.g. Theory of Planned behavior) can help us gain a deeper understanding of how intentions translate into actual behavior in situations with multiple important goals if they are coupled with more generic control beliefs, such as self-efficacy. This main finding has both theoretical and practical implications.