Search results

Although manufacturer-initiated rewards are widely used to secure distributors’ compliance, the spillover effect on unrewarded distributors (i.e. observers) in the same distribution channel is under-researched. Using insights from social learning theory, this paper aims to investigate how manufacturer-initiated rewards affect observers’ expectation of reward and shape observers’ compliance toward the manufacturer. Furthermore, this paper explores how such effects are contingent upon distributor relationship features.

To test the hypotheses, hierarchical multiple regression and bootstrapping analyses were performed using survey data from 280 Chinese distributors.

The magnitude of a manufacturer-initiated reward to a distributor stimulates expectation of reward among observers, which enhances compliance; observers’ expectation of reward mediates the impact of reward magnitude on compliance. Moreover, network centrality (of the rewarded peer) negatively moderates the positive impact of reward magnitude on observers’ expectation of reward, whereas observers’ dependence (on the manufacturer) positively moderates this dynamic.

Manufacturers should pay attention to the spillover effects of rewards. Overall, they should use rewards of appropriate magnitude to show willingness to recognize outstanding distributors. This will inspire unrewarded distributors, which will then be more compliant. Furthermore, manufacturers should know that specific types of distributor relationship features may significantly vary the spillover effects.

This study illuminates the spillover effects of manufacturer-initiated reward by opening the “black box” of the link between reward magnitude and observers’ compliance and by specifying the effects’ boundary conditions.

The study aims to develop a theoretical model that portrays the antecedents of emotional attachment in the travel context by combining branding, marketing and information systems theories.

The authors gather empirical data through a Web-based questionnaire from 236 respondents. The proposed theory-driven model is examined empirically by using confirmatory factor analysis and structural equation modeling.

The findings suggest that social media rewards and benefits impact users’ brand commitment. Social media interactivity and rewards help building a stronger brand image. Brand commitment and brand image, in turn, affect emotional attachment positively.

Other unexamined constructs may add to the explanation of building brands using social media platforms. As this is an exploratory study in relation to enhancing emotional attachment in an online travel setting, other constructs such as brand page commitment, annoyance, social benefits and telepresence may be considered in future studies.

Practitioners might encounter ways to influence favorable perceptions and brand commitment when consumers use social media sites. The model addresses questions regarding the significant role of social media activities on influencing brand image and brand commitment that in turn influence the development of a strong emotional attachment.

This study examined the effects of social media activities including interactivity, psychological benefits and rewards on brand image and brand commitment, and the effects of brand image and brand commitment on emotional attachment in the travel context. The results offer further verification for the theory-based model presented in the study. Evidently, statistically significant and meaningful associations exist among the factors.

The key contribution of this study is that it presents and validates a theory-driven model that reveals the antecedents of sustainable emotional attachment. The proposed framework stresses the positive relationships among constructs and offers research basis for expansion in other settings.

本论文结合品牌、营销、以及信息系统等理论, 建立了一个理论模型以描述在旅游情境中影响情感依恋的各种要素。

作者通过网络问卷的形式收集了236份数据, 并且通过验证性因素分析和结构方程模型的手段来实际测量以理论为基础建立的模型。

本论文研究结果表明社交媒体的奖励和好处影响用户的品牌承诺。社交媒体的交互性以及奖励帮助建立更强的品牌形象。品牌承诺和品牌形象对情感依恋有着积极促进的作用。

社交媒体平台的品牌建立模型还存在一些其他变量尚未开发测量。本论文只是开拓了网络旅游平台的情感依恋研究的方向, 其他变量比如品牌专页承诺、烦恼、社交好处、以及网真等, 应该在未来的研究中得以深入。

执业者可能会遇到多种方法, 通过社交媒体网站的方式影响消费者的主观感知和品牌承诺。本论文提出的模型可以帮助执业者解决关于社交媒体活动对品牌形象和品牌承诺显著影响从而达到强烈情感依恋效果的诸多问题。

本论文研究了旅游产业中, 社交媒体活动, 包括交互性、心理好处和奖励对品牌形象和品牌承诺的影响, 以及品牌形象和品牌承诺对情感依恋的影响。研究结果进一步深入测量了提出的理论模型。很显然, 数据分析结果表明模型结构之间存在显著有意义的联系。

本论文最重要的贡献在于它提出并验证了一个理论模型, 显示可持续情感依恋的动力起因。本论文提出的模型强调了结构之间的积极联系, 并且为在其他情境中的研究延申做出启示。

The literature includes a vast amount of research on both innovation and business networks; however, the empirical knowledge of their intersection – innovation networks and their management – is still scarce. This empirical study aims at increasing the knowledge of management of innovation networks by mapping characteristics of management approaches of two case companies. These companies operate in the software business and develop their products in inter‐organizational networks. Special attention is paid to differences in the management approaches between the case companies.

The present empirical article is based on analysis of two case companies representing very different and contrasting approaches to management of innovation networks. The empirical study is conducted among SMEs in the software business.

As a result of the analysis, several aspects of management of innovation networks are identified and their nature explained. These aspects are: duration of the network; rewards from the network; fundamental meaning of the network; the nature of the networked organization; planning, control, and trust; and hierarchies, authority, and coordination. These aspects are powerful in mapping and explaining the characteristics of innovation network management.

Various management practices are suggested and discussed in the context of each of the identified aspects of innovation network management.

Synthesizes the ideas of the “transformational change” and “learning organization” literature. The concept of the action learning organization is presented as a bridge between learning and transformation as it involves collaborative questioning by organizational members of their own actions. Discusses the characteristics of an action learning organization in terms of its bias for reflection‐in‐action, formation of learning alliances, development of external networks, multiple reward systems, creation of meaningful information, individual empowerment, leadership and vision. The knowledge‐generating organization is the one which is most likely to be able to survive both equilibrium and chaos.

The purpose of this paper is to examine power from a multi‐theoretical perspective by comparing and analyzing different views and definitions of power in order to use the findings for further studying this construct as a key element for managerial purposes in the interorganizational context.

An overview of the literature is conducted examining the power from sociological, psychological and managerial perspectives specifying views on power, its sources and consequences of its use offered by selected theories.

This paper presents the opinion that the definitions of power by different theories resemble each other and the main differentiations in conceptualizations of power stem mostly from the differences in capturing sources and consequences of power. Power generally refers to the ability, capacity or potential to get others do something, to command, to influence, to determine or to control the behaviors, intentions, decisions or actions of others in the pursuit of one's own goals or interests despite resistance, as well as to induce changes.

The fact that power can be used as an effective tool to coordinate and manage others appears to be largely ignored in the literature. In order to understand how to use it for these purposes, it is necessary to define power, which is an elusive concept that has a variety of meanings and definitions, and there seems to be much disagreement to the precise meaning of power.

The higher education sector in Sweden has, over decades, faced increasing demands in terms of efficiency rates in research, as well as increasing demands in the international competition for external revenue. These demands have influenced academic career trajectories and postdoctoral tracks as well as the everyday work of doctoral students. The purpose of this paper is to investigate how doctoral students express and challenge subjectivity in the present context of research education.

The authors depart from the overall understanding that doctoral students’ lines of actions in research education depend on and form assemblages and, thus, define an academic institution. By re-analysing eight in-depth interviews, they illustrate how doctoral students from different milieus not only comply but also challenge, use border-crossings and change directions in research education.

The results show that some of these doctoral students try to act as loyal and satisfied, especially in regard to their supervisors, whereas others use coping strategies and resistance. It is illustrated that when some of the students use “unsecure” molecular lines, they appear more open to redefining possibilities and change, in comparison with those on more stable molar lines. Those acting on molar lines sometimes express a lack of emotional (productive) engagement, even though this particular group tend to more often get access to rewarded assemblages. These patterns are partly gender-related.

The tension between finding more stable lines and spaces for change is apparent in doctoral students’ subjectivity, but also how this tension is related to gender. The women doctoral students appear not only more mobile but also in a sense more alert than their men peers. This offers insights in how actions define and redefine not only academic institutions but also different subjectivities.

In the present, given the manifold demands on academic institutions, new insights and methodological approaches are necessary to illustrate how contemporary changes affect research education and the everyday life of doctoral students.

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.

A neural network controller is proposed for the motion control of robot manipulators with force/torque feedback signals. This controller is trained with reinforcement learning algorithms and a model is extracted from the synaptic weights within the neural network. This model is continuously refined by the feedback signals to ensure its validity even in a stochastic and non‐stationary environment. With this model and the real‐time force/torque feedback data, the robot can acquire a fine skill for a particular assembly task for which it is trained.

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.