Search results

The purpose of this paper is to investigate the effects of Blockchain on the customer order management process and operations. There is limited understanding of the use and benefits of Blockchain on supply chains, and less so at processes level. To date, there is no research on the effects of Blockchain in the customer order management process.

A twofold method is followed. First, a Blockchain is programmed and implemented in a large international firm. Second, a series of simulations are built based on three scenarios: current with no-Blockchain, 1-year and 5-year Blockchain use.

Blockchain improves the efficiency of the process: it reduces the number of operations, reduces the average time of orders in the system, reduces workload, shows traceability of orders and improves visibility to various supply chain participants.

The research is based on a single in-depth case that has the scope to be tested in other contexts in future.

This is the first study that demonstrates with real data from an industrial firm the effects of Blockchain on the efficiency gains, reduction on the number of operations and human-processing savings. A detailed description of the Blockchain implementation is provided. Furthermore, this research shows a list of the resources and capabilities needed for building and maintaining a Blockchain in the context of supply chains.

This is the first study that demonstrates with real data from an industrial firm the effects of Blockchain on the efficiency gains, the reduction in the number of operations and human-processing savings. A detailed description of the Blockchain implementation is provided. This paper contributes to the resource-based view of the firm, by demonstrating two new competitive valuable capabilities and a new dynamic capability that organisations develop when implementing and using Blockchain in a supply–demand process. It also contributes to the information processing theory by highlighting the analytics capabilities required to sustain Blockchain-related operations.

Employee voice is crucial for organizations to identify problems and make timely adjustments. However, promoting voice in organizations is challenging. This study aims to investigate how social media use (SMU) in the workplace affects employee voice by examining its intrinsic mechanisms and boundary conditions. Specifically, this study examines the mediating roles of social identifications and the moderating effects of job-social media fit on the relationship between SMU and social identifications.

This study conducted a survey of 348 employees in China.

First, SMU affects voice through social identifications. Second, distinct identifications have different effects on voice, such that organizational identification positively affects employee voice, while relational identification positively affects promotive voice and negatively affects prohibitive voice. Third, when social media is highly suitable for the job, the positive effect of work-related SMU on organizational identification is strengthened, while the positive effect of social-related SMU on organizational identification is weakened.

The results indicate that different identifications have distinct impacts on voice. Additionally, this study reveals a double-edged sword effect of SMU on voice through different social identifications. Further, job-social media fit moderates the relationship between SMU and social identifications. These findings have important implications for organizations adopting social media.

The purpose of the present study is to develop a new numerical framework that can predict the supersonic base flow more accurately, including the development of axisymmetrically separated shear layer and recompression shock. To this end, two aspects are improved and combined, i.e. a newly self-adaptive turbulence eddy simulation (SATES) turbulence modeling method and a high-order discretization numerical scheme. Furthermore, the performance of the new numerical framework within a general-purpose PHengLEI software is assessed in detail.

Satisfactory prediction of the supersonic separated shear layer with unsteady wake flow is quite challenging. By using a unified turbulence model called SATES combining high-order accurate discretization numerical schemes, the present study first assesses the performance of newly developed SATES for supersonic axisymmetric separation flows. A high-order finite differencing-based compressible computational fluid dynamics (CFD) code called PHengLEI is developed and several different numerical schemes are used to investigate the effects on shock-turbulence interactions, which include the monotonic upstream-centered scheme for conservation laws (MUSCL), weighted compact nonlinear scheme (WCNS) and hybrid cell-edge and cell-node dissipative compact scheme (HDCS).

Compared with the available experimental data and the numerical predictions, the results of SATES by using high-order accurate WCNS or HDCS schemes agree better with the experiments than the results by using the MUSCL scheme. The WCNS and HDCS can also significantly improve the prediction of flow physics in terms of the instability of the annular shear layer and the evolution of the turbulent wake.

The small deviations in the recirculation region can be found between the present numerical results and experimental data, which could be caused by the inaccurate incoming boundary layer condition and compressible effects. Therefore, a proper incoming boundary layer condition with turbulent fluctuations and compressibility effects need to be considered to further improve the accuracy of simulations.

The present study evaluates a high-order discretization-based SATES turbulence model for supersonic separation flows, which is quite valuable for improving the calculation accuracy of aeronautics applications, especially in supersonic conditions.

For the first time, the newly developed SATES turbulence modeling method combining the high-order accurate WCNS or HDCS numerical schemes is implemented on the PHengLEI software and successfully applied for the simulations of supersonic separation flows, and satisfactory results are obtained. The unsteady evolutions of the supersonic annular shear layer are analyzed, and the hairpin vortex structures are found in the simulation.

The purpose of this paper is to quantify how mobile app usage relates to the unique characteristics of behavioral orientations and content types, focussing on the interrelationship among content usage in the context of in-app purchase.

Using a large-scale data set of individual content usage in a particular music mobile app, the author builds a simultaneous equation panel data model to examine dynamic interdependent usage of mobile app.

The paper finds a positive temporal effect of self-oriented content usage (download) on other-oriented content usage (gift), based on behavioral orientation, and also a temporal interdependence between external (ringtone) and internal usage (mp3) based on types of content. The paper also finds that the fourth generation communications standard increases content usage in this mobile app.

These findings provide useful insights for mobile app developers, mobile network operators, content providers, and mobile device manufacturers.

This paper is one of the first to consider and empirically test the interrelationship between various kinds of content usage in music apps.

In this paper we discuss the impact of e‐commerce on the design and construction industry, with specific reference to how information technology will affect competition and competitive advantage in the industry. This paper continues the exploration of a concept that we have been working on for several years, namely ‘…that information technology is evolving from a tool that incrementally improves “back‐office” productivity to an essential component of strategic positioning that may alter the basic economics, organizational structure and operational practices of facility management organizations and their interactions with service providers (architects, engineers and constructors)’ (Johnson & Clayton, Automation in Construction, 8, 1998, 3). We first review industry trends and then develop a generalized model for thinking about strategy and e‐commerce based on our previous research as well as the well‐known framework developed by Michael Porter. Next we apply this framework to the discussion of several case studies and draw preliminary conclusions about the viability of the model for future research.

The purpose of this paper is to elaborate on the differences between price partitioning and drip pricing with regard to their influence on price recall, purchase intentions and fairness perceptions. In many industries, sellers advertise low prices and reveal other surcharges sequentially as the customer goes through the buying process. To date, little is known about how these sequential, or drip-pricing, techniques influence consumer behavior.

The study is based on an experimental between-subjects design (N = 95) with two groups. The data collection was conducted with a mixed scenario/stimuli-based online survey for a virtual travel agent.

The findings reveal that underestimation of the total price of an offering is significantly weaker when prices are presented sequentially rather than partitioned. In addition to reduced purchase intentions, drip pricing may negatively affect fairness perceptions when consumers feel deceived by the seller.

The study replicates findings of previous research on price partitioning but is one of the first empirical studies to examine the influence of sequence in price presentations. With this focus, the study opens up new avenues for pricing research.

Enterprises use social media for their daily work. The use of social media in the workplace is crucial for social connections, the growth and evolution of the enterprise, and it opens up new avenues for voice behavior. Employee voice involves the expression of ideas or opinions towards enterprise and is beneficial for employee work and enterprise development. Extant studies of voice behavior usually focus on the leadership and employee factors. However, the internal mechanism of voice behavior, especially the interrelationship between different kinds of social media use and voice behavior has not been well investigated. To fill that research gap, this study analyzes the internal mechanism of voice behavior, taking the effects of social media use and social capital into consideration.

Using structural equation model, this study collected data from employees using social media and analyzed the data using the software of Smartpls 3.0, SPSS and AMOS, in order to analyze the internal mechanism of voice behavior among employees.

Based on social capital theory, this study investigates the relationship between social media use, social capital and voice behavior, and provides some insights into the mechanism of voice behavior. The social media use, social capital and voice behavior are divided into several kinds in order to clarify the internal mechanism of voice behavior more comprehensively. The empirical results show that: (1) Social media use for both work and social-related purposes could positively affect employees’ promotive and prohibitive voice behaviors. (2) Social capital mediates the relationship between social media use and voice behavior. (3) In the process of social media use influencing employees’ voice behavior, employees of different genders and ages show significant differences in social capital and voice behavior.

This study explored the internal mechanism of voice behavior, which could help to elicit the relationship between social media use and voice behavior. By integrating the roles of social capital, individual differences, this study could uncover the deep internal mechanism of employee voice behavior more comprehensively, broadening social capital theory and enriching the researches of voice behavior among employees.

Purpose: The evidence regarding the effects of the COVID-19 lockdown on domestic violence is mixed. Studies using hotline call services identify an increase on domestic violence, while studies using police reports find a decrease. One limitation is that most of these studies came from diverse regions using different types of data sources. The purpose of this study is to use two separate data sources to study this question in the same region, and to contribute to the discussion for potential mechanisms that explain this mixed evidence.

Methodology: This study estimates the effects of the COVID-19 lockdown on domestic violence in Mexico City. The authors use two separate data sources: hotline calls and official police reports. Our empirically strategy is based on a difference-in-differences methodology and an event-study design.

Findings: As a consequence of the COVID-19 lockdown, hotline calls for psychological domestic violence increase by 17%, while police reports of domestic violence decrease by 22%. To reconcile these discrepancies between hotline calls and police reports, the authors consider several potential mechanisms. The authors find suggestive evidence that the increase in psychological domestic violence is related to financial stress. Further, the results of this study indicate that the reduction in police reports is related to women facing more barriers to report their abusive intimate partners during the lockdown.

Value: These results confirm that the variation observed in the existing literature is related to the type of data being used. The mixed evidence suggests that more women suffer from psychological domestic violence as captured by hotline calls, while women encounter more barriers to report their abusive husbands to the police as captured by the official police reports.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.