Transaction Characteristics Research Articles

The value of branding for B2B service firms—The shareholders' perspective

Increasingly fierce competition in many B2B service industries led managers to seek a competitive advantage through branding, as many firms operating in B2C markets have done. Although advertising spending in B2B service industries has soared and branding could be relevant for customers' purchase decisions, the distinct characteristics of B2B service transactions can make the creation of brand equity challenging. Conceptual arguments thus both support and question the net financial value of B2B service brands and, since empirical research on that value is missing, managers are left with no agreed upon and validated guidance. Drawing on observations of the brands and shareholder values of 380 U.S.-based public B2B service firms, this study introduces a shareholder perspective to empirically examine the financial value of B2B service brands. Results reveal that B2B service brands have positive value to shareholders. However, the extent of that value depends on a firm's branding strategy (corporate branding vs. multi-branding) in conjunction with the type of services offered (professional services vs. other B2B services) but not in conjunction with the breadth of services offered (degree of diversification). This research provides new insights for researchers and managers with regard to B2B service brands' value and important contingency factors.

A Study on the Characteristics of Residential Preference Area according to Apartment Price and Trading Volume in Busan

In this study, the housing market in Busan was subdivided based on apartment prices and trading volume, demonstrated significant factors that affect the pattern of residential preferred areas and significant factors affecting the difference in housing prices between regions based on the spatial-temporal variability in 2010 and 2015. The spatial range of the analysis was set based on 205 administrative areas of 16 districts throughout Busan. The variables that were found to have a significant effect on housing prices and transaction volume were reviewed through prior research, and among them, variables that could be built based on administrative areas were determined as analysis data. Variables consist of population characteristics, household characteristics, housing characteristics, and transaction characteristics, all set at 18. The Temporal range was set in 2010 and 2015 in line with the survey period of the population and housing census of Korea provided data by the spatial units of the administrative areas. The distribution of large and medium-sized apartments(exclusive area 101~135㎡) in Busan has been confirmed to be discriminatory locally based on prices and trading volume. In addition, the distribution of transactions based on housing transactions in 2010 and 2015 was able to confirm that high-priced houses were concentrated and anchored in particular areas, and that its influence was gradually spreading around.

OPTIMALISASI BANYUWANGI IN YOUR HAND MELALUI FITUR PENGINTEGRASIAN GPS DAN TAX REFUND GUNA MENINGKATKAN CUSTOMER SATISFACTION

Banyuwangi In Your Hand is an application connected with fiber optic that has been connecting 280 kilometers in 28 districts in Banyuwangi Regency. However, the increasing numbers of the tourist is not in line with the numbers of application download. Therefore, the application needs to be evaluated in terms of the feature and program. This study examines the process of information update related to the location of tourism objects in Banyuwangi. This is a qualitative descriptive study. Data were collected from literature study and interviews with the tourists visiting Banyuwangi. This study proposed the idea of integrating GPS in Banyuwangi In Your Hand application to increase the performance of information update about tourism spots in Banyuwangi. This study also proposed to add tax refund application on the transaction feature in order to increase the value of the Banyuwangi In Your Hand application. The aim of the addition of both systems in Banyuwangi In Your Hand application was to increase customer satisfaction
 
 Keywords: Banyuwangi In Your Hand, GPS, tax refund, customer satisfaction

The Choice of the Methods of Engineering Contracts’ Contracting and the Prevention of Speculative Behaviors

For a long time, the choice of the methods of engineering contracts’ contracting has mainly relied upon the method of Marginal Analysis which is the strong point of Classical Economics, and the feature of analysis is the fixed conditions and demands of engineering, and after comparing the costs of different methods of engineering outsourcing, the chief issue of saving costs is solved. When analyzing the speculations of the field of engineering construction like dishonesty etc, the Marginal Analysis has displayed conspicuous insufficiency. This may be relevant with the hypothesis of human behaviors of Marginal Analysis, however, sufficient consideration of human’s limited rationality and speculative tendencies may come closer to the realistic engineering transaction activities. When considering the methods of engineering outsourcing, people need to pay attention to the management abilities of different methods of subcontracting regarding speculations and the effect of application of limited rationality. The delegate of New Institutional Economics, Williamson suggested a method of analysis, choosing the asset specificity, limited rationality and speculation as three important influencing factors when studying market and the problem of corporation signing the contracts, and proposed four contract models using different combinations of the above mentioned three factors in order to prevent speculative behaviors. This essay tries to apply this theory and referring to the transaction characteristics of Chinese construction engineering industry, a discussion has been presented about the influence of the ways of construction engineering contracting upon how to prevent the speculative behaviors of the contractors.

How did land titling affect China’s rural land rental market? Size, composition and efficiency

Based on 2010–2015 official surveys conducted by the Ministry of Agriculture, we apply the difference-in-differences method to study how the latest land titling reform in China affected the land rental market. While we cannot attribute the rapid growth of the land rental market during this period to the land titling reform, the reform did have a significant impact on the composition of renters and certain features of the rental transaction. Specifically, we show that the land titling reform led to an increase in land transferred to agricultural enterprises and cooperatives, and a decrease in the probability of households renting land out to other households (which were likely to be relatives and friends). The land titling reform also encouraged households to use written contracts and to charge pecuniary rents, although it did not affect the duration of the contract. Our results suggest that the land titling reform improved the efficiency of the land rental market by promoting the formalisation of rental contracts and by facilitating the allocation of land to more productive users.

Anti-money laundering and counter-terrorist financing threats posed by mobile money

PurposeThe purpose of this paper is to explore the various characteristics of mobile money transactions and the threats they present to anti-money laundering (AML) and counter terrorist financing regimes.Design/methodology/approachA thorough literature review was conducted on mobile money transactions and the associated money-laundering and terrorist financing threats. Four key themes were identified in relations to the three stages of money laundering and effective law enforcement.FindingsThe findings indicate that as money laundering and terrorist financing transactions continue to gravitate towards the weaknesses in the financial system, mobile money provides yet another avenue for criminals to exploit. Risk factors associated with anonymity, elusiveness, rapidity and lack of oversights were all integral considerations in building an effective AML regime. The use of cash is considered a higher threat than mobile money prior to implementation of systems and controls.Practical implicationsThis rapidly changing environment of how individuals manage their money during transactions is set to further explode globally, which poses new problems for regulators and governments alike. Unless there is a unified concentration to heighten global awareness, the imposing threat of mobile money is set to increase at a rapid rate if appropriate actions are not taken.Originality/valueThe findings from this study can be used to gain greater insights on mobile money transactions and raise further awareness of the ever-increasing threat to global financial integrity.

Human communication theory: a five-dimensional model

ABSTRACTRecognition of the world of Human communication (HC) is necessary for fathoming Mankind’s promise. Theorising about HC under the prevailing definition is often one-dimensional and linear. A model constructed from a pentad of dimensions demonstrates that HC can best be fathomed under a model that recognises its multidimensionality (five dimensions – 5D) and the unified features of transaction of meaning-sharing in its entirety. This article looks eclectically at examples from the world of human communication to show the efficacy of this model in making appraisals of some germane facets of singular and collective cultural dissimilitude and advancement.

Grenade-Cauchy operator integrated artificial bee colony optimisation for QoS-based reliable web service composition

Majority of the dynamic web service composition techniques in the literature addresses the problem of web service composition through the perspectives of quality of service (QoS) or transactional characteristics. Grenade with Cauchy operator integrated artificial bee colony optimisation (GCO-ABC) algorithm is proposed as an attempt of developing a dynamic service composition involved computation methodology that depends on the integration of transactional and QoS-based properties of web services. GCO-ABC algorithm is applied on the work flow sequence of web service composition developed as a constrained directed acyclic graph for determining the near optimal solution with efficacy and it is also predominant in comparison to the traditional ant colony optimisation algorithm (ACOM) and its variant improved ant colony optimisation algorithm (IACOM). The determined results investigated through empirical and simulation means infer that the proposed scheme is not only efficient but also capable of approximating the searching solutions into a much more optimal candidate solution.

Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions

Online fraud transaction has been a big concern for e-business platform. As the development of big data technology, e-commerce users always evaluate the sellers according to the reputation scores supplied by the platform. The reason why the sellers prefer chasing high reputation scores is that high reputations always bring high profit to sellers. By collusion, fraudsters can acquire high reputation scores and it will attract more potential buyers. It has been a crucial task for the e-commerce website to recognizing the fake reputation information. E-commerce platforms try to solve this continued and growing problem by adopting data mining techniques. With the high development of the Internet of Things, big data plays a crucial role in economic society. Big data brings economic growth in different domains. It supplies support to the management and decision-making ability in e-business through analyzing operational data. In online commerce, the big data technology also helps in providing users with a fair and healthy reputation system, which improves the shopping experience. This paper aims to put forward a conceptual framework to extract the characteristics of fraud transaction, including individual- and transaction-related indicators. It also contains two product features: product type and product nature. The two features obviously enhance the accuracy of fraud detection. A real-world dataset is used to verify the effectiveness of the indicators in the detection model, which is put forward to recognize the fraud transactions from the legitimate ones.

Features of the Contact Transaction of Ingredients on Inter-Industry Cooperation Project

Features of the Contact Transaction of Ingredients on Inter-Industry Cooperation Project

Design and performance analysis of various feature selection methods for anomaly‐based techniques in intrusion detection system

Intrusion detection system (IDS) is essential for the network; the intruder can steal sensitive information about networks. The IDS must have the ability to take care of large and real‐time data. The predicted rate must be high based on the available attribute. This work deals with a real intrusion detection problem, by its behavior. In this paper, we developed a hybrid model, which can detect intrusion by its action. We used an NSL‐KDD data set, the multiclass problem and binary problems are 25% tested. This model can be used to guess the availability of intrusion, able to determine the scope of intrusions based on the transaction of data in the network; training requires optimal features of a network transaction. The accuracy of the model is better for both binary class for the multiclass in NSL‐KDD data set. The complication of false data alarm rates is the most significant challenge in the IDS system, and it may be the low false rate or high false rate. Proposed work also addresses this problem. The first step that data will be filtered by Vote algorithm, the Information Gain will get associated with a base learner, to choose the necessary features, which directly affects the accuracy of the model. It uses the following classifier: RandomTree, REPTree, RandomForrest AdaBoostM1, Meta Pagging, DesicionStump, J48, LMT, Bagging, and Naive Bayes. On the based on the proposed model, it is observed as low false rate, high accuracy.

Detecting Fraudulent Bank Account Based on Convolutional Neural Network with Heterogeneous Data

Detecting fraudulent accounts by using their transaction networks is helpful for proactively preventing illegal transactions in financial scenarios. In this paper, three convolutional neural network models, i.e., NTD‐CNN, TTD‐CNN, and HDF‐CNN, are created to identify whether a bank account is fraudulent. The three models, same in model structure, are different in types of the input features. Firstly, we embed the bank accounts’ historical trading records into a general directed and weighted transaction network. And then, a DirectedWalk algorithm is proposed for learning an account’s network vector. DirectedWalk learns social representations of a network’s vertices, by modeling a stream of directed and time‐related trading paths. The local topological feature, generating by accounts’ network vector, is taken as input of NTD‐CNN, and TTD‐CNN takes time series transaction feature as input. Finally, the two kinds of heterogeneous data, being integrated into a novel feature matrix, are fed into HDF‐CNN for classifying bank accounts. The experimental results, conducted on a real bank transaction dataset, show the advantage of HDF‐CNN over the existing methods.

Novel Financial Capital Flow Forecast Framework Using Time Series Theory and Deep Learning: A Case Study Analysis of Yu’e Bao Transaction Data

Appropriate monetary liquidity is important for financial institutions. When institutions lack adequate cash flow for customer redemption, their income will decrease, their reputation will be affected, and they may even go bankrupt. However, the opposite extreme in which more cash is reserved than needed may result in lost opportunities to make successful investments. This study uses Yu'e Bao transaction data to investigate a method for forecasting financial capital flow. Yu'e Bao, which is a financial product launched by Alibaba, faces the core challenge of maximizing commercial profits to reduce investment risks. Liquidity risk is considered the main factor in Yu'e Bao's investment strategy. First, a linear model called YEB_ARIMA is proposed by determining the autocorrelation (ACF) and partial autocorrelation (PACF) parameters, which are optimized by the grid search method. Second, a deep learning model called YEB_LSTM is introduced to strengthen the expressiveness of the model that yields nonlinear transaction features. Then, a hybrid learning method called YEB_Hybrid is applied to improve the original weak classifiers. This model includes both a linear combination and logistic regression learning. Third, a set of experiments and analyses are conducted based on subscription and redemption datasets to demonstrate that the hybrid model achieves an accuracy of 84.39% and 84.36%, respectively, under a variety of evaluation indexes. Finally, various proposed fund reserve ratios are provided based on capital forecasts.

Articulation-Based Accruals and Audit Fees

This paper examines the relation between audit fees and accruals from a balance sheet auditing perspective. We argue that the underlying economic characteristics of various transactions, as reflected in the articulation-based accruals in Casey et al. (2017), are predictably associated with audit fees. We are the first to demonstrate that total accruals have a non-linear relation with audit prices, with both large positive and large negative total accruals driving audit fees higher. We further disaggregate total accruals based on their accounting and economic characteristics. Accruals originating from the balance sheet and the cash flow statement have a positive relation with audit fees, while the statement of owner’s equity accruals are negatively related to audit fees. We then decompose total accruals into five articulation-based accruals and find that working capital accruals, discontinued operations accruals, and net financial accruals have a linear relationship with audit fees. In contrast, equity investment accruals and net noncurrent operating accruals (mainly driven by depreciation expenses) have a non-linear relationship with audit fees.

Improved Cosine Similarity-based Artificial Bee Colony Optimization scheme for reactive and dynamic service composition

Reliable and dynamic composition of web services is considered as essential for ensuring continuous services to the users since they are responsible for integrating varsity of applications in spite of their independency. The significant development in web services domain in the last two decades enable the option of devising novel service composition and service selection schemes for optimal performance and success rate of dynamic web service composition. Majority of the research works proposed for dynamic composition of web services was confirmed to be formulated using the characteristics of Quality of Service (QoS) or Transactional features of workflow. Improved Cosine Similarity-based Artificial Bee Colony Optimization Scheme for Web Service Composition (ICS-ABCO-WSC) is proposed for integrating the characteristics of QoS and transactional features for determining optimal candidate service solution from the workflow modeled graph generated during reactive service composition. ICS-ABCO-WSC is proved to enhance the rate of exploitation and exploration by incorporating opposition learning in employee bee phase and, combinatorial search strategical equations and enhance rate factor in the employee and onlooker bee phase respectively. The success rate and optimality index derived using experimental investigation of ICS-ABCO scheme is proved to be 26% and 32% excellent to compared baseline graph-modeled web service composition techniques. This improvement is realized in ICS-ABCO-WSC due to its potential of enhancing the precision and acceleration rate of converging solution achieved in Artificial Bee Colony Optimization technique.

When Does Partitioned Pricing Lead to More Favorable Consumer Preferences?: Meta-Analytic Evidence

Evidence of the impact of partitioned pricing is contradictory. Research indicates that partitioning a price into multiple components can result in more favorable preferences, due to a lower recalled price, or less favorable preferences, due to unfavorable surcharge evaluations. To explain these divergent effects, the authors examine the role of price presentation moderators, which reflect how managers convey prices to consumers (e.g., Is the total price present or absent?), magnitude moderators, which reflect the actual prices charged (e.g., What is the surcharge magnitude?), and contextual moderators, which reflect nonprice transaction characteristics (e.g., Is the product category hedonic or utilitarian?). A meta-analysis of 17 years of partitioned pricing research examining 149 observations in 27 papers (N = 12,878) suggests that consumers respond more favorably to partitioned pricing than to all-inclusive pricing when the total price is absent, as the price level increases, when the surcharges are typical for the product category, when the surcharges are perceived as offering high benefit, and when the product category is utilitarian.

Trust, Cultural Health Capital, and Immigrants’ Health Care Integration in Norway

Trust is a decisive feature of social interactions, transactions, and relationships, yet the implications of trust in integration processes has largely escaped sociological inquiry. Based on interviews and focus group discussions with Somali immigrants in Norway, this article examines trust in the context of health care integration. Using empirical examples from a birth-clinic, the MMR-vaccine, and everyday consultancy, the study highlights that the relationship between Somalis and Norwegian health care providers is characterized by a pervasive, mutual unfamiliarity. This unfamiliarity translates to a reliance on selected Norwegian-Somalis who by commanding two health care cultures can engage as bridge-builders to negotiate associated barriers. The article argues that the combination of immigrant background and formal health knowledge constitutes a type of cultural health capital. Although tapping into this capital extends the health care system and enhances Somalis’ ability to invest trust and benefit from entitlements, a shared background is not synonymous with trust.

Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity

With the popularization of online shopping, transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of their BPs. Markov chain models are popular to represent BPs of users, which is effective for those users whose transaction behaviors are stable relatively. However, with the development and popularization of online shopping, it is more convenient for users to consume via the Internet, which diversifies the transaction behaviors of users. Therefore, Markov chain models are unsuitable for the representation of these behaviors. In this paper, we propose logical graph of BP (LGBP) which is a total order-based model to represent the logical relation of attributes of transaction records. Based on LGBP and users’ transaction records, we can compute a path-based transition probability from an attribute to another one. At the same time, we define an information entropy-based diversity coefficient in order to characterize the diversity of transaction behaviors of a user. In addition, we define a state transition probability matrix to capture temporal features of transactions of a user. Consequently, we can construct a BP for each user and then use it to verify if an incoming transaction is a fraud or not. Our experiments over a real data set illustrate that our method is better than three state-of-the-art ones.

Exploring the use of ‘third countries’ in proliferation networks: the case of Malaysia

Abstract‘Third countries’ are frequently exploited by those involved in networks to transfer proliferation-sensitive technologies, allowing procurement agents to obscure the end user or vendor located in the proliferating state, and to deceive industry, export licensing officials, and intelligence services. While ‘third countries’ frequently feature in illicit transactions, the academic literature exploring the roles played by entities in these jurisdictions is limited. Building on the sanctions busting literature, this article proposes a loose typology considering the ways in which third countries can be exploited by proliferation networks. The typology is illustrated using three cases involving entities based in Malaysia – A. Q. Khan’s nuclear black market network, and Iran and North Korea’s efforts to procure and market WMD-related and military goods. These cases are used to generate insights into proliferators’ selection of ‘third country’ hubs. The article argues that while exploitation of third countries by proliferation networks is a similar, but distinct phenomenon to trade-based sanctions busting, hubs of both activities share characteristics. Furthermore, the article argues that other factors beyond the lax regulatory environment, such as level of development, and personal connections, are often as important in driving the decisions of proliferation networks. The article concludes with implications for nonproliferation policy.

A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection

Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements the reorganization of raw transaction features to form different convolutional patterns. Its significance is that different feature combinations entering the convolution kernel will produce different derivative features. The advantage of this model lies in taking low dimensional and nonderivative online transaction data as the input. The whole network consists of a feature sequencing layer, four convolutional layers and pooling layers, and a fully connected layer. Verifying with online transaction data from a commercial bank, the experimental results show that the model achieves excellent fraud detection performance without derivative features. And its precision can be stabilized at around 91% and recall can be stabilized at around 94%, which increased by 26% and 2%, respectively, comparing with the existing CNN for fraud detection.