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This paper aims to examine the perceived influence of knowledge management (KM) practices on the success of e-government initiatives. This paper proposes a framework depicting the overall perspective of the interactions between the environment and KM practices and associated processes in the context of e-government.

A conceptual framework was built to set the stage for empirical analysis, which included four major constituents: IT infrastructure, administrative issues, KM practices and e-government projects success. A sample of 181 civil servants completed a survey measuring the factors included in the research model. Structural equation modeling technique was used to test the model.

Results have identified IT infrastructure and administrative issues as significant predictors of e-government projects’ success, where the relationship was mediated by KM practices. The model explained 52.7 per cent of the variance in e-government success.

Governments need to enforce policies to encourage KM practices and make available the needed infrastructure for such environment. The sample size and the new Arabic survey used in the study are the major limitations, where more research is encouraged to validate the instrument and generalize the findings to different environments.

This study is the first in Jordan, and one of the few that related e-government to KM practices by proposing a comprehensive model that sums the factors related to such relationship. Its value stems from its sample of public employees and the support of its proposed framework.

This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

The purpose of this research is to highlight the impact of integrative leadership behaviors on network performance in local government networks.

The data were retrieved from a survey conducted on 362 local government network leaders in Italy. Their leadership behaviors were compared with the level of network performance anonymously self-reported.

The findings show that high frequency in the usage of a specific category of behavior does not always lead to high performance in local government networks. Moreover, leadership behaviors leading to highly performing networks are not always engaged most frequently by networks' leaders.

This research gives an empirical contribution to a neglected topic: network leadership. Moreover, the authors attempt to highlight how it is able to influence network performance.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

This study aims to illuminate the contribution of neurophysiological techniques in the field of marketing and consumer decision-making and to highlight avenues and research questions that marketing researchers can take advantage of from neuroscience and psychology to inform marketing phenomena.

The authors first reviewed the roots and definition of consumer neuroscience. Then, the authors outlined the main characteristics of the most commonly used neurophysiological tools (namely, skin conductance, facial electromyography, electrocardiogram, eye-tracking, electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy, magnetoencephalography and transcranial magnetic stimulation) with a special emphasis on their advantages and weaknesses. Finally, the authors propose the development of research lines that could be implemented by marketing researchers with an appropriate application and understanding of tools and theories of neuroscience and psychology.

The authors propose research questions to be addressed within four thematic areas: opportunities in product decisions (predicting product purchasing decisions, consumer responses to branding efforts and packaging), pricing, communication and retailing scenarios. The authors also incorporate insights into the complementarity of neurophysiological tools to traditional ones and situations in which these tools are useful for enhancing marketing theory. The authors finally shed light on the moral–ethical criticisms of this new branch of marketing.

To the best of the authors’ knowledge, this research constitutes the first study in identifying the research opportunities that marketing researchers could take advantage from neuroimaging and physiological tools to inform marketing theory and practice.

Esta investigación tiene como objetivo esclarecer la contribución de las técnicas neurofisiológicas en el campo del marketing y la toma de decisiones de los consumidores y destacar las vías y preguntas de investigación que los investigadores de marketing pueden aprovechar de la neurociencia y la psicología para informar sobre los fenómenos del marketing.

En primer lugar, revisamos el origen y la definición de la neurociencia del consumidor. A continuación, esbozamos las principales características de las herramientas neurofisiológicas más utilizadas (a saber, la conductancia, la electromiografía facial, el electrocardiograma, el seguimiento ocular, la electroencefalografía, la resonancia magnética funcional, la espectroscopia funcional en el infrarrojo cercano, la magnetoencefalografía y la estimulación magnética transcraneal), haciendo especial hincapié en sus ventajas y debilidades. Finalmente, se propone el desarrollo de líneas de investigación que podrían ser implementadas por los investigadores de marketing con una adecuada aplicación y comprensión de las herramientas y teorías de la neurociencia y la psicología.

Proponemos preguntas de investigación para ser abordadas dentro de cuatro áreas temáticas: oportunidades en las decisiones de producto (predicción de las decisiones de compra de productos, respuestas de los consumidores a los esfuerzos de marca y envasado), precios, comunicación y distribución. También incorporamos ideas sobre la complementariedad de las herramientas neurofisiológicas con las tradicionales y las situaciones en las que estas herramientas son útiles para mejorar la teoría del marketing. Por último, arrojamos luz sobre las críticas ético-morales a esta nueva rama del marketing.

本研究旨在阐明神经生理学技术在营销和消费者决策领域的贡献, 并强调营销研究人员可以从神经科学和心理学中利用的途径和研究问题, 以告知营销现象。

我们首先回顾了消费者神经科学的根基和定义。然后, 我们概述了最常用的神经生理学工具（即皮肤电导率、面部肌电图、心电图、眼球追踪、脑电图、功能性磁共振成像、功能性近红外光谱、脑磁图和经颅磁刺激）的主要特点, 特别强调了它们的优势和劣势。最后, 我们提出了研究路线的发展, 这些路线可以由营销研究人员通过适当的应用和理解神经科学和心理学的工具和理论来实施。

我们提出了四个主题领域的研究问题：产品决策中的机会（预测产品购买决策、消费者对品牌推广工作的反应和包装）、定价、沟通和零售场景。我们还纳入了对神经生理学工具与传统工具的互补性的见解, 以及这些工具对加强营销理论有用的情况。最后, 我们对这个新的营销分支的道德伦理批评进行了说明。

纸张类型 – 研究论文

The purpose of the study is to explore, with anchorage in theories about the normalization of corruption, under what conditions blockchain technology can mitigate corruptive practices of multinational enterprises (MNEs) in emerging markets (EMs).

By synthesizing a technological perspective and theory on corruption, the authors examine the feasibility of blockchain for fighting corruption in MNEs’ business operations in EMs.

Blockchain technology is theorized to have varying mitigating effects on the rationalization, socialization and institutionalization of corruption. The authors provide propositions describing the effects and the limitations of blockchain for mitigating corruption in EMs.

This paper offers a perspective for how to tackle acute business problems and social problems pronounced in international business but also prevailing elsewhere.

The study contributes to literature in international management by systematically exploring how and under what conditions blockchain can mitigate the normalization of corruption.

The purpose of this two-part study is to systematically review, analyze and critically synthesize the current state of empirical research on knowledge loss induced by organizational member turnover (KLT).

This study is based on using a systematic literature review methodology reported in Part I.

Part II of this study contributes to the advancement of KLT scholarship by offering: an integrative narrative of KLT coping and preventive mechanisms as well as factors affecting them; an organizing framework of KLT empirical literature; and suggestions for future research, which are discussed with respect to the content, based on the proposed framework and by extending contextual dimensions of “who”, “where” and “when”, as well as use of theories and methods.

This study has limitations related to inclusion/exclusion criteria used for creating the review sample and the “Antecedents–Phenomenon–Outcomes” logic used to synthesize the findings.

Part II of this study offers a systematic synthesis of KLT empirical research with respect to KLT coping and preventive mechanisms and a discussion of opportunities for future research.

While a growing body of literature reveals the prevalence of men's harassment and abuse of women online, scant research has been conducted into women's attacks on each other in digital networked environments. This chapter responds to this research gap by analyzing data obtained from qualitative interviews with Australian women who have received at times extremely savage cyberhate they know or strongly suspect was sent by other women. Drawing on scholarly literature on historical intra-feminism schisms – specifically what have been dubbed the “mommy wars” and the “sex wars” – this chapter argues that the conceptual lenses of internalized misogyny and lateral violence are useful in their framing of internecine conflict within marginalized groups as diagnostic of broader, systemic oppression rather than being solely the fault of individual actors. These lenses, however, require multiple caveats and have many limitations. In conclusion, I canvas the possibility that the pressure women may feel to present a united front in the interests of feminist politics could itself be considered an outcome of patriarchal oppression (even if performing solidarity is politically expedient and/or essential). As such, there might come a time when openly renouncing discourses of sisterhood and feeling free to disagree with, and even dislike, other women might be considered markers of liberation.