Search results

The purpose of this paper is to present the optimal double sampling attribute plan using the weighted Poisson distribution.

For the given AQL and LQL, sum of producer’s and consumer’s risks have been attained. Based on the weighted Poisson distribution, the sum of these risks has been optimized.

In the final inspection, the producer and the consumer represent the same party. So, the sum these two risks should be minimized. In this paper, the sum of risks has been tabulated using the weighted Poisson distribution for different operating ratios. These tabulated values are comparatively less than the sum of risks derived using Poisson distribution.

The sampling plan presented in this paper is particularly useful for testing the quality of finished products in shop floor situations.

The purpose of this paper is to present the single sampling attribute plan for given acceptance quality level (AQL) and limiting quality level (LQL) involving minimum sum of risks using weighted Poisson distribution.

For the given AQL and LQL, sum of producer's and consumer's risks have been attained. Based on weighted Poisson distribution, the sum of these risks has been arrived at, along with the acceptance number and the rejection number. Also, the operating characteristic function for the single sampling attribute sampling plan, using weighted Poisson distribution, has been derived.

In the final inspection, the producer and the consumer represent the same party. So, the sum these two risks should be minimized. In this paper, the sum of risks has been tabulated using weighted Poisson distribution for different operating ratios. These tabulated values are comparatively less than the sum of risks derived using Poisson distribution.

The sampling plan presented in this paper is particularly useful for testing the quality of finished products in shop floor situations.

Seeks to determine whether hybrid inspection performance is superior to manual performance in a generic manufacturing setup. Explains the design of an experiment to achieve this comparison. Results include the fact that the hybrid method took substantially less time and caused fewer inspection errors. Notes that cost factors would need to be carefully considered before selection of a preferred method but that ultimately the hybrid method should be the logical choice.

The aim of this study is to provide a theoretical extension to the private‐collective model of information sharing along with an empirical test with users of a social bookmarking website.

The paper includes a survey of 112 users of an actual bookmarking site recruited through an online research panel firm. The survey consisted of scales adapted from the literature as well as scales developed by the authors.

The results indicate that contributions to a social bookmarking site are a combination of intentional and unintentional contributions. A significant predictor of intentional public contributions of bookmarks is an egoistic motivation to see one as competent by contributing valuable information. However, there is also a significant but negative relationship between altruism and public contribution whereby users concerned with the needs of others limit their public contributions.

The sample consists of users of a particular social bookmarking site (Yahoo!'s MyWeb). Therefore, the results may not be generalizable to other social bookmarking websites, different types of social networks, or other contexts lacking the public/private option for contributions. Second, since the data comes from a cross‐sectional survey, as opposed to a longitudinal study, the causal relations posited in the model and substantiated with the statistical analyses can only be inferred based on the authors’ theoretical development. Third, although the size of the sample (112 respondents) is appropriate for PLS analysis it may have been insufficient to detect other significant relationships.

Administrators of social bookmarking sites should incorporate incentive and feedback mechanisms to inform contributors whether they contributions have been used (for example, with times viewed) and/or deemed useful (with numeric or qualitative ratings).

The results suggest that both selfish motivations associated with the need to feel competent (egoism), as well as selfless concerns for the needs of other users (altruism) drive intentional contributions to the public repository in social bookmarking systems. These two counterbalancing forces indicate that a mix of egoism and altruism is crucial for the long‐term sustainability of social web sites based on information sharing.

This study provides theoretical explanations and empirical evidence of egoism and altruism as significant explanations for cooperation in private‐collective models, such as the ones represented by social bookmarking systems.

Drawing on sensemaking and emotion regulation research, the purpose of this paper is to reconceptualize core contributor withdrawal (CCW) in the context of online peer-production communities (OPPCs). To explain the underlying mechanisms that make core contributors withdraw from these communities, the authors propose a process theory of contributor withdrawal called the core contributor withdrawal theory (CCWT).

To support CCWT, a typology of unmet expectations of online communities is presented, which uncovers the cognitive and emotional processing involved. To illustrate the efficacy of CCWT, a case study of the English version of Wikipedia is provided as a representative OPPC.

CCWT identifies sensemaking and emotion regulation concerning contributors’ unmet expectations as causes of CCW from OPPCs, which first lead to declined expectations, burnout and psychological withdrawal and thereby to behavioral withdrawal.

CCWT clearly identifies how and why important participation transitions, such as from core contributor to less active contributor or non-contributor, take place. By adopting process theories, CCWT provides a nuanced explanation of the cognitive and affective events that take place before core contributors withdraw from OPPCs.

CCWT highlights the challenge of online communities shifting from recruiting new contributors to preventing loss of existing contributors in the maturity stage. Additionally, by identifying the underlying cognitive and affective processes that core contributors experience in response to unexpected events, communities can develop safeguards to prevent or correct cognitions and emotions that lead to withdrawal.

CCWT provides a theoretical framework that accounts for the negative cognitions and affects that lead to core contributors’ withdrawal from online communities. It furthers the understanding of what motivates contributing to and what leads to withdrawal from OPPC.

This paper investigates online evaluation processes on peer-to-peer platforms to highlight how online peer evaluation enacts neoliberal subjects and collectives.

The paper uses netnography (Kozinets, 2002) to study the online community of Airbnb. It is also based on 18 interviews, mostly with Airbnb users, and quantitative data about reviews.

Results indicate that peer-to-peer platforms constitute biopolitical infrastructures. They enact and consolidate narcissistic entrepreneurs of the self through evaluation processes and consolidating a for-show community. Specifically, three features make evaluation a powerful neoliberal agent. The object of evaluation shifts from the service to the user's own worth (1). The public nature of the evaluation (2) and symetrical accountability between the evaluator and the evaluatee (3) contribute to excessively positive reviews and this keeps the market fluid.

This paper calls for problematization of the idea of sharing in the so-called “sharing economy”. What is shared on peer-to-peer platforms is the comfort of engaging with people like ourselves.

This paper contributes to the literature on online accounting by extending consideration of evaluation beyond the review process. It also stresses that trust in the evaluative infrastructure is fostered by narcissistic relationships between users, who come to use the platform as a mirror. The peer-to-peer context refreshes the our knowledge on evaluation in a corporate context by highlighting phenomena of standardized spontaneity and euphemized evaluation language. This allows evaluation processes to incorporate a market logic without having to fuel competition.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The purpose of this paper is to propose a method for statistical inference on data from power law distributions in order to explain behavior and social phenomena associated with web‐based social spaces such as discussion forums, question‐and‐answer sites, web 2.0 applications and the like.

The paper starts by highlighting the importance of explaining behavior in social networks. Next, the power law nature of social interactions is described and a hypothetical example is used to explain why analyzing sub‐sets of data might misrepresent the relationship between variables having power law distributions. Analysis requires the use of the complete distribution. The paper proposes logarithmic transformation prior to correlation and regression analysis and shows why it works using the hypothetical example and field data retrieved from Microsoft's Netscan project.

The hypothetical example emphasizes the importance of analyzing complete datasets harvested from social spaces. The Netscan example shows the importance of the logarithmic transformation for enabling the development of a predictive regression model based on the power law distributed data. Specifically, it shows that the number of new and returning participants are the main predictors of discussion forum activity.

This paper offers a useful analysis tool for anyone interested in social aspects of the Internet as well as corporate intra‐net systems, knowledge management systems or other systems that support social interaction such as cellular phones and mobile devices. It also explains how to avoid errors by paying attention to assumptions and range restriction issues.

The paper aims to argue that leadership by police managers is needed to stimulate and encourage knowledge sharing in police investigations, and to report an empirical study of what management roles are most important in investigations.

A research model was designed based on six management roles and a set of hypothesized relationships. A survey measuring management roles and knowledge sharing attitude was conducted in Norway. Respondents were senior investigation officers.

Only one management role was found to be a significant determinant of knowledge sharing in police investigations based on the sample used in this survey research within the Norwegian police force: the spokesman role was the only significant role. As a spokesman, the senior investigation officer extends organizational contacts to promote acceptance of the unit and the unit's work within the organization of which they are a part.

The low response rate of 20 percent may make it difficult to draw strong conclusions. Unfortunately, the authors have no information about what kinds of non‐response bias might be present (significant variation between the sample and the population). Future research should be more consistent in identifying the population.

While police investigations (of organized crime, trafficking, narcotics, economic crimes, homicide, etc.) need a stimulating internal structure for knowledge sharing, investigations depend on knowledge sharing with relevant persons and departments outside the unit as well to succeed.

Rather than stressing the importance of leadership in general to stimulate knowledge management, this paper is original as it applies a set of management roles to empirically study where leadership makes a difference for knowledge sharing attitudes.

In light of the recent increase in the scholarly attention given to meaningful engagement with gamified information systems (IS), this research explores the definition and measurement of meaningful engagement as well as its role in predicting employees’ knowledge contributions via gamified knowledge management systems (KMSs).

The authors conducted two empirical studies. Study 1 develops a measure of meaningful engagement and evaluates its validity and reliability. Drawing on the literature on user engagement and work gamification theory, Study 2 places meaningful engagement in a nomological network and assesses the construct’s utility for predicting the quantity and quality of knowledge contributions via a gamified KMS.

The results show that meaningful engagement encompasses five specific dimensions: intense involvement, sense of meaning, self-discovery, pursuit of excellence, and personal expressiveness. The results also indicate that fostering meaningful engagement, which goes beyond hedonic and instrumental engagement, is essential to enhance the quality and quantity of knowledge contribution.

This research contributes to the literature on gamification by drawing scholarly attention to meaningful engagement as a parsimonious yet powerful construct that complements the notions of hedonic and instrumental engagement with KMSs. Although previous studies have highlighted the significance of meaningful engagement with gamified IS, little effort has been made to develop a scale to measure meaningful engagement. The scale the authors have developed will help researchers precisely measure users’ meaningful engagement and systematically examine its role in gamified systems compared to that of other forms of engagement. The study also has practical implications, as the results can inform future design strategies to enable the successful implementation of gamified KMSs that facilitate knowledge contribution in the workplace.

The development of new constructs is the starting point for theoretical development. This research responds to the call to conceptualize meaningful engagement with gamified IS.