Coding Techniques Research Articles

High-Capacity Double-Sided Square-Mesh-Type Chipless RFID Tags

This paper presents a novel methodology for designing high-capacity frequency domain chipless RFID tags based on the backscattering principle. The tag consists of multiple square open-loop resonators loaded with a varying number of square metallic patches to form a mesh structure. Thus, in contrast to conventional designs, the overall physical size of each resonator remains fixed and does not change with respect to its operating resonant frequency. The RCS response of the proposed tag can be easily manipulated by varying the loading factor of each resonator. The tag size is further miniaturized by placing resonators on both sides of the substrate used. The tag configuration with resonators arranged in the form of a single row (4 × 1) printed on both sides of the substrate is finally chosen for maximum robustness and efficiency. The frequency shift coding (FSC) technique is used to encode more than one bit per resonator by using segments within sub-bands. The proposed tag encodes 16-bit data in a frequency band from 5.9 to 10.5 GHz and has a very high code density of 23.51 bits/cm2 and a spectral efficiency of 3.47 bits/GHz. The design methodology is novel and leads to a very efficient chipless RFID tag that can be used in a variety of high-data-capacity applications.

Hybridizing qualitative coding with natural language processing and deep learning to assess public comments: A case study of the clean power plan

Public comments are a rich source of data on attitudes toward public policy, but the scale poses major challenges for qualitative analyses. Supervised deep learning and natural language processing potentially enable classification of these data but require high quality labeled inputs. This study investigates whether hybridizing rigorous qualitative coding methods with machine learning approaches can classify large amounts of policy-oriented text data (public comments) while keeping manual effort tractable. Using a convolutional neural network, we evaluate spatiotemporal variation in themes and expressed public attitudes toward a specific US climate policy, the Clean Power Plan (CPP), which was proposed in 2014 and repealed in 2019 without ever taking effect. Public comments were solicited for both the proposal and proposed repeal across eight cities, representing a large and highly targeted dataset on dynamic public attitudes toward the CPP. Rigorous manual coding and data augmentation techniques enabled good model performance (F1 scores of 0.71 and 0.81, respectively, for sentiment and topic classification), even with a very small training set. We find that most speakers supported the CPP despite its eventual repeal and uncover notable rhetorical trends like competing narratives of justice for those affected by climate change versus fossil fuel host communities.

Evaluating public health strategies for climate adaptation: Challenges and opportunities from the climate ready states and cities initiative

Evaluation generates critical evidence about the effectiveness of health-focused climate adaptation, who receives what benefits, and how to improve program quality. However, using evaluation to improve climate adaptation outcomes with timeliness and context-specificity is uniquely challenging. The United States Centers for Disease Control and Prevention supports health departments to implement adaptation initiatives through the Climate-Ready States and Cities Initiative (CRSCI) grant and minimize negative health impacts of climate change via the Building Resilience Against Climate Effects (BRACE) framework, which includes evaluation. In this paper, we present current evaluation practice by describing the health-focused adaptation actions that were evaluated among CRSCI recipients, the evaluation approaches they used, and the barriers and enablers encountered. Using a mixed methods approach, we abstracted annual progress report and standardized performance measure data to characterize evaluation activities across 18 grant recipients using basic quantitative descriptive analysis. Through structured interviews with three grant recipients and standard team-based qualitative coding and analysis techniques, we developed qualitative case studies to explore evaluation barriers and enablers in richer context. Recipients reported 76 evaluations over the reporting period (2018–2021). Evaluated programs commonly addressed extreme weather (50.0%), followed by heat (27.6%). The most common purpose was to monitor implementation or improve delivery (57.9%). Case studies highlighted barriers to successful evaluation such as limited specialized evaluation expertise and staff time. Enablers included staff motivation to justify program expansion, strong relationships with community partners, and use of evaluation plans. Case studies revealed diverse strategies to seek input from stakeholders disproportionately impacted by climate change and to reduce health disparities. The experiences of CDC grant recipients provide an opportunity to better understand the barriers and enablers of climate and health evaluation practice and to identify practical strategies to expand the value of evaluation in this nascent field.

Proficient matrix codes for error detection and correctionin 8-port network on chip routers

This paper verifies the applicability of the proposed code to dynamic Network on Chips that have variable faulty blocks with runtime suggesting an online error detection mechanism with adaptive routing algorithm that bypasses faulty components dynamically and the router architecture uses additional diagonal state indications for the reliable network on chip (NoC) operation. In NoC, the permanently faulty routers are disconnected to enable high runtime throughput as data packets are not lost due to self-loopback mechanism. The proposed proficient matrix codes use the capabilities of decimal matrix code technique with minimum check bits for maximum error correction capability. The proposed code is compared with existing codes such as decimal matrix codes, modified decimal matrix codes and parity matrix codes. The codes are developed in verilog hardware description language and simulated in the Xilinx ISE 14.5 tool. This proficient matrix code proves to be efficient for multiple adjacent error detection and correction with trade off in delay. Also 65% code rate is achieved with 22.73% less redundant bits that occupy less area by atleast 11.78%. The codes when used for increased data sizes like 8, 16, 32, and 64 bits, the power delay product decreased by atleast 1.74%.

Energy Efficient Wireless Crowd Labeling: Joint Annotator Clustering and Power Control

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

Distributed Video Coding Based on Polar Codes

In this letter we present an improved distributed video coding (DVC) scheme based on polar coding techniques. Firstly, we adapt log-likelihood ratios (LLRs) for DVC with integer implementation of a discrete cosine transform (DCT). We propose a computationally efficient and numerically stable modification of these LLRs based on the simplified methods of polar codes decoding. We show that on average this approach provides 0.3 dB PSNR gain for DVC with LDPC accumulated (LDPCA) codes. Secondly, we introduce the nested shortened polar codes construction algorithm. We demonstrate that replacement of LDPCA by polar codes improves PSNR by 0.1 dB on average, whereas, for videos with relatively high motion level, the gain reaches up to 0.23, 0.39 and 0.55 dB for Group of Pictures (GOP) lengths 2, 4 and 8 frames, respectively. Finally, experimental results demonstrate that DVC with polar codes and Tal-Vardy list decoder operates up to two times faster than DVC with LDPCA code and belief propagation (BP) decoder.

Adaptive Modulation and Coding: a Brief Review of the Literature

This work provides a general review of Adaptive Modulation and Coding (AMC) techniques that optimize the utilization of available resources and parameters in wireless communication systems. AMC or link adaptation empowers communication systems to employ appropriate modulation and coding schemes in order to enhance link spectrum utilization over time-varying channels. In addition, the proper selection of Modulation and Coding Schemes (MCS) diminishes the Bit Error Rate (BER) of systems and reduces the required transmit power through which the interference to other users is minimized. This paper conducts a state-of-the-art review of the existing AMC technologies. The reviews provided in this paper shed light on the trends of research in AMC and bring focus to related techniques. First, the existing constraints and the related phenomena in advanced wireless communication, including channel capacity, path loss, fading, and interference, are thoroughly investigated to understand further and improve the AMC systems. Moreover, several scenarios of utilizing the AMC methods are provided in order to demonstrate the extent of the effectiveness and importance of accurate AMC. Lastly, the challenges, the likely problems, the research gaps, and the potential avenues for future work are declared.

Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm.

The length of hospital stay (LOS) is a significant indicator of the quality of patient care, hospital efficiency, and operational resilience. Considering the importance of LOS in hospital resource management, this research aims to improve the accuracy of LOS prediction using hyperparameter optimization (HPO). Expert physicians and related studies were reviewed to determine the variables affecting LOS. The electronic medical records of 200 patients in the department of internal medicine of a hospital in Iran were collected randomly. As the performance of machine learning (ML) models can vary based on the characteristics of the features, several models were applied and evaluated in this study. In particular, k-nearest neighbors (KNN), multivariate regression, decision tree (DT), random forest (RF), artificial neural network (ANN), and XGBoost have been evaluated and improved. The genetic algorithm (GA) was applied to optimize the tree-based models. In addition, the dummy coding technique, sometimes called the One-Hot encoding, was used to encode categorical features to increase prediction accuracy. Compared with other algorithms, the XGBoost model optimized by GA (XGB_GA) achieved higher accuracy and better prediction performance. The mean and median of absolute errors in the test dataset for this model were 1.54 and 1.14 days, respectively. In other words, the XGB_GA model reduced the mean absolute error by 37%, which is beneficial in the reliable design of a clinical decision support system.

Channel Polarization Scheme for Ocean Turbulence Channels in Underwater Visible Light Communication

Underwater wireless communication technology plays an important role in marine environment monitoring and ecological protection. Underwater optical wireless communications (UWOCs) can currently achieve a transmission distance of hundreds of meters, and the rate can reach hundreds of Mbps or even Gbps, with low power consumption and high-speed features. In addition, UWOC also has the advantages of a small transceiver size and strong anti-electromagnetic interference ability, which is especially suitable for scenarios where underwater volume and power consumption are relatively limited. However, UWOC systems face problems such as unstable transceiver ends, ocean turbulence, and so on, resulting in reduced communication reliability and limited transmission distance. Establishing a stable and reliable communication link is critical to extending the communication distance of the UWOC system. In this paper, a model of ocean turbulence channels is established based on the power spectrum inversion method. The transmission characteristics of orbital angular momentum (OAM) light in an ocean turbulence channel are studied, then the mode selection of OAM light is determined. At the same time, the polarization coding technique is applied to the underwater OAM communication system for the first time. The simulation results show that this scheme can effectively extend the communication distance and reduce the system bit error rate.

Projective Linear Codes From Some Almost Difference Sets

Projective linear codes are a special class of linear codes whose duals have minimum distance at least 3. The columns of the generator matrix of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> projective code over finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{q}$ </tex-math></inline-formula> can be viewed as points in the projective space <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {PG}(k-1, {\mathbb {F}}_{q})$ </tex-math></inline-formula> . Projective codes are of interest not only because their duals have good error correcting capability but also because they may be related to interesting combinatorial structures. The objective of this paper is to construct projective linear codes with five families of almost difference sets. To this end, the augmentation and extension techniques for linear codes are used. The parameters and weight distributions of the projective codes are explicitly determined. Several infinite families of optimal or almost optimal codes including MDS codes, near MDS codes, almost MDS odes and Griesmer codes are obtained. Besides, we also give some applications of these codes.

Intelligent Reconfigurable Surface-Aided Space-Time Line Code for 6G IoT Systems: A Low-Complexity Approach

Intelligent reconfigurable surfaces (IRS) have attracted much attention from both industry and academia due to their performance improving capability and low complexity for 6G wireless communication systems. In this letter, we introduce an IRS-assisted space-time line code (STLC) technique. The STLC was introduced as a promising technique to acquire the optimal diversity gain in 1×2 single-input multiple-output (SIMO) channel without channel state information at receiver (CSIR). Using the cosine similarity theorem, we propose a novel phase-steering technique for the proposed IRS-assisted STLC technique. We also mathematically characterize the proposed IRS-assisted STLC technique in terms of outage probability and bit-error rate (BER). Based on computer simulations, it is shown that the results of analysis shows well match with the computer simulation results for various communication scenarios.

Students Counselling Guidance Using Digital Platforms and Satisfaction for Online Learning

The study aimed to obtain an analysis of the use of digital platforms and the level of satisfaction through student prospects during dare learning which has been carried out for 2 years at the State Islamic Institute of Sheikh Nurjati Cirebon. This study was designed using mixed methods, namely qualitative and quantitative methods. The participants in this study were students of the Faculty of Ushuluddin Adab and Da'wah of Institute Agama Islam Negeri Syekh Nurjati Cirebon with a total of 758 students. They are students in the even semester of the 2020-2021 and 2021-2022 academic years. Data collection was carried out using a questionnaire distributed to participants, observations and documents. The data obtained were analyzed using quantitative data analysis using MS Excel. For the qualitative analysis, the inductive coding technique was used. The results of the data analysis found two main conclusions, namely, first, the use of digital platforms is related to digital competence, and the level of student motivation and responsibility is lower. Meanwhile, the online learning environment and digital technology facilities are quite good. The second conclusion is the level of satisfaction which shows students are dissatisfied with the aspects of interaction, evaluation, communication, and the usefulness of online learning. The results of these findings also recommend that teachers pay attention to the level of needs, abilities, and readiness of students in carrying out online learning.

Decision‐making factor interactions influencing climate migration: A systems‐based systematic review

AbstractGlobal migration and mobility dynamics are expected to shift in the coming decades as a result of climate change. However, the extent to which migration is caused by climate hazards, in contrast or addition to other intervening factors, is a point of debate in literature. In this study, we conducted a systematic literature review to identify and consolidate factors which directly and indirectly influence climate change migration. In our review of the literature, we found a total of 21 economic, environmental, demographic, political, social, and personal intervening decision‐making factors which affect climate migration. Causal interactions between these factors were identified using an axial qualitative coding technique called purposive text analysis. By combining causal links, a semi‐quantitative causal loop diagram was created that represented factor interaction and feedback within the “climate migration system.” Using this model, we highlight influential feedback loops and point to how intervention strategies may cause downstream effects. This research helps to address calls for a better understanding of the complex decision‐making dynamics in climate migration. In particular, results from our causal feedback loops show that intervention strategies targeted toward economic factors such as financial capital and livelihoods, as well as food security, would have the greatest impact in assisting climate‐affected communities. These results help inform climate migration policy and aid planners in the future to better understand the interconnected system of factors that lead to the emergent outcome of climate migration.This article is categorized under: Vulnerability and Adaptation to Climate Change &gt; Learning from Cases and Analogies The Social Status of Climate Change Knowledge &gt; Climate Science and Decision Making

Influencing aspects of clinical nurses' interactive continuous learning behaviour based on in-service online network videos: A grounded theory approach

ObjectivesThis study explores the influencing aspects of nurses' continuous interactive learning behaviour based on In-Service online network videos to facilitate their knowledge development and improve their engagement in online learning. Also, to provide a theoretical model for enhancing the quality of learning and teaching by promoting online nursing education. DesignThe qualitative research design of grounded theory methodology (Glaser and Strauss, 1967) was used to develop a theory of clinical nurses' interactive continuous learning behaviours. MethodA grounded theory approach and qualitative research method were adopted in this research. A three-level coding technique was used to extract data, which involved collecting 16,018 pieces of “Synchronous online feedback data” original data from interactive online in-service learning videos. The synchronous online feedback data were analysed using a constant comparative method and then utilised to construct the theoretical model of influencing aspects for nurses' interactive continuous learning. There were 11,083 Synchronous online feedback data of Clinical Nurses (N = 132 participants) based on 32 interactive learning video series from a single centre. The sampling method was based on initial purposive and then theoretical sampling techniques. The data were analysed and extracted using a three-level coding technique with a constant comparison analysis method. ResultsThe study identified four major categories of influencing aspects for nurses' satisfaction in online learning. These categories include 1. individual aspects; 2. curriculum aspects; 3. teachers' aspects; and 4. nurses' interactive behaviour. These aspects were found to influence the learning satisfaction among nurses and nurses' interactive continuous learning behaviours. ConclusionThe findings provide a reference for maximising the value of In-Service online video learning resources and the sustainable development of educational informatisation. In addition, interactive continuous learning behaviour can promote adherence to a positive learning attitude and build an excellent online learning ecology.

Ghanaian Students’ Perceptions of Chinese Language Learning

Purpose: Chinese language proficiency presents myriads of opportunities. However, students’ perceptions influence their decisions to learn the language and since perceptions vary in different contexts, this study examined how Ghanaian students perceive Chinese language learning.&#x0D; Methodology: The qualitative approach, with the descriptive research design was used for the study. The target population was the University of Cape Coast students who were neither reading BA Chinese language programme nor taking free Chinese language and culture classes. Using the convenience sampling technique, twenty-two students who volunteered to partake in the study were selected. Data for the study were collected using an open-ended questionnaire. The data collected were analysed using the thematic content analysis based on the techniques of opening and axial coding.&#x0D; Findings: The results revealed that Ghanaian students generally had positive perceptions of Chinese language learning, and those with unfavourable perceptions are likely enroll onto a form of Chinese learning course if they become aware of the benefits associated with proficiency in Chinese language.&#x0D; Recommendations: The study recommend that outreach programmes should be instituted by Chinese language centres in Ghana to sensitise the public on the benefits of Chinese language learning. The study also recommend more research to be done on Chinese language.

Randomness extraction in computability theory

In this article, we study a notion of the extraction rate of Turing functionals that translate between notions of randomness with respect to different underlying probability measures. We analyze several classes of extraction procedures: (1) a class that generalizes von Neumann’s trick for extracting unbiased randomness from the tosses of a biased coin, (2) a class based on work of by Knuth and Yao (which more properly can be characterized as extracting biased randomness from unbiased randomness), and (3) a class independently developed by Levin and Kautz that generalizes the data compression technique of arithmetic coding. For the first two classes of extraction procedures, we identify a level of algorithmic randomness for an input that guarantees that we attain the extraction rate along that input, while for the third class, we calculate the rate attained along sufficiently random input sequences.

Drawing is an important tool to learn context-based histology in an integrated undergraduate medical curriculum

ObjectivesTo determine if learning histology by drawing is superior to learning by looking through a microscope only. MethodsSecond year MBBS students were divided by simple random sampling into Groups A and B. Each group comprised 50 students. This mixed-methods study was conducted in an 8-week module. For the first 4 weeks, students in Group A learned histology by drawing, whereas Group B learned by seeing the text and microscopic images. For the last 4 weeks, groups were swapped by crossover design. The impact of learning by drawing was assessed by multiple choice question (MCQ) test I and test II at the end of 4 and 8 weeks, respectively. Statistical analyses of the data were conducted with SPSS version 23. The scores obtained in test I and test II were analyzed by the independent samples t-test. The paired samples t-test was applied to scores obtained by the same subject when they learned with drawing and no drawing strategies. To assess the impact of drawing on learning histology, a focus group study was conducted in six participants selected by purposive sampling. Responses to the semi-structured interview questions were analyzed by qualitative research techniques of coding, categorizing, and generation of themes. ResultsThe independent samples t-test showed that there was no statistically significant difference in the mean scores obtained by Groups A and B in test I and test II. However, there was a statistically significant difference when the subject learned histology by drawing compared to no drawing, as shown by the paired samples t-test. The results from the focus group study revealed that drawing had a positive impact on knowledge retention and understanding the basic concepts of histology for its application in the clinical context. ConclusionDrawing-based learning in histology helps with the application of basic knowledge in the clinical context.

Satellite Image Compression Guided by Regions of Interest

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform.

Mapping the state of the art of creative cluster research: a bibliometric and thematic analysis

ABSTRACT The notion of creative clusters has become the focus of a growing number of policy initiatives aimed at revitalizing economies by means of creativity. However, despite their prominence in policy discourse, creative clusters are still a ‘fuzzy’ concept, defined and treated differently in different strands of research. To address these disparities, this paper presents a systematic literature review of creative cluster research (CCR), with the aim of: (1) exploring the state of the art in the field, (2) pointing out some important limitations, and (3) outlining a future research agenda. A total of 355 articles published between 1986 and 2019 were analysed, drawing upon a combination of manual coding, bibliometric analysis, and text mining techniques. This multi-method approach allowed us to provide both a meta-analysis of CCR and an exploration of its thematic content. In so doing, our paper contributes to a comprehensive understanding of how creative clusters have been studied over time, both broadly and in relation to different creative sectors and geographical contexts. Moreover, through the identification of research gaps and boundaries of knowledge in the field, it points to key methodological and conceptual development issues to be addressed in future studies.

Contribution to team and community in crowdsourcing contests: a qualitative investigation

PurposeThe research study intends to gain a better understanding of members' behaviors in the context of crowdsourcing contests. The authors examined the key factors that can motivate or discourage contributing to a team and within the community.Design/methodology/approachThe authors conducted 21 semi-structured interviews with Kaggle.com members and analyzed the data to capture individual members' contributions and emerging determinants that play a role during this process. The authors adopted a qualitative approach and used standard thematic coding techniques to analyze the data.FindingsThe analysis revealed two processes underlying contribution to the team and community and the decision-making involved in each. Accordingly, a set of key factors affecting each process were identified. Using Holbrook's (2006) typology of value creation, these factors were classified into four types, namely extrinsic and self-oriented (economic value), extrinsic and other-oriented (social value), intrinsic and self-oriented (hedonic value), and intrinsic and other-oriented (altruistic value). Three propositions were developed, which can be tested in future research.Research limitations/implicationsThe study has a few limitations, which point to areas for future research on this topic. First, the authors only assessed the behaviors of individuals who use the Kaggle platform. Second, the findings of this study may not be generalizable to other crowdsourcing platforms such as Amazon Mechanical Turk, where there is no competition, and participants cannot meaningfully contribute to the community. Third, the authors collected data from a limited (yet knowledgeable) number of interviewees. It would be useful to use bigger sample sizes to assess other possible factors that did not emerge from our analysis. Finally, the authors presented a set of propositions for individuals' contributory behavior in crowdsourcing contest platforms but did not empirically test them. Future research is necessary to validate these hypotheses, for instance, by using quantitative methods (e.g. surveys or experiments).Practical implicationsThe authors offer recommendations for implementing appropriate mechanisms for contribution to crowdsourcing contests and platforms. Practitioners should design architectures to minimize the effect of factors that reduce the likelihood of contributions and maximize the factors that increase contribution in order to manage the tension of simultaneously encouraging contribution and competition.Social implicationsThe research study makes key theoretical contributions to research. First, the results of this study help explain the individuals' contributory behavior in crowdsourcing contests from two aspects: joining and selecting a team and content contribution to the community. Second, the findings of this study suggest a revised and extended model of value co-creation, one that integrates this study’s findings with those of Nov et al. (2009), Lakhani and Wolf (2005), Wasko and Faraj (2000), Chen et al. (2018), Hahn et al. (2008), Dholakia et al. (2004) and Teichmann et al. (2015). Third, using direct accounts collected through first-hand interviews with crowdsourcing contest members, this study provides an in-depth understanding of individuals' contributory behavior. Methodologically, this authors’ approach was distinct from common approaches used in this research domain that used secondary datasets (e.g. the content of forum discussions, survey data) (e.g. see Lakhani and Wolf, 2005; Nov et al., 2009) and quantitative techniques for analyzing collaboration and contribution behavior.Originality/valueThe authors advance the broad field of crowdsourcing by extending the literature on value creation in the online community, particularly as it relates to the individual participants. The study advances the theoretical understanding of contribution in crowdsourcing contests by focusing on the members' point of view, which reveals both the determinants and the process for joining teams during crowdsourcing contests as well as the determinants of contribution to the content distributed in the community.