Crowdsourcing for Sustainable Urban Logistics: Exploring the Factors Influencing Crowd Workers’ Participative Behavior

With crowd logistics becoming a crucial part of the last-mile delivery challenge in many cities, continued participation of crowd workers has become an essential issue affecting the growth of the crowd logistics platform. Understanding how people are motivated to continue their participation in crowd logistics can provide some clarity as to what policies and measures should be undertaken by the industry to support its further growth. Using the Push-Pull-Mooring (PPM) theory, we developed a research model to explain the factors influencing crowd workers' participative behavior. Survey data from 455 crowd workers were analyzed using SmartPLS3.0 software. The results show monetary rewards and trust have a significant positive impact on the willingness of crowd workers to continue participating in crowd logistics, while work enjoyment from previous work and entry barriers for work have a significant negative impact. Trust plays an intermediary role between monetary incentives and crowd workers' willingness to continue participating. Based on the findings of this study, we recommend that crowd logistics platforms should offer reasonable monetary incentives and keep these under constant review, build a high degree of trust and cooperation with their crowd workers, and initiate activities geared towards promoting satisfaction at work

Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Incentivizing Exploration with Selective Data Disclosure

We study the design of rating systems that incentivize (more) efficient social learning among self-interested agents. Agents arrive sequentially and are presented with a set of possible actions, each of which yields a positive reward with an unknown probability. A disclosure policy sends messages about the rewards of previously-chosen actions to arriving agents. These messages can alter agents' incentives towards exploration, taking potentially sub-optimal actions for the sake of learning more about their rewards. Prior work achieves much progress with disclosure policies that merely recommend an action to each user, but relies heavily on standard, yet very strong rationality assumptions. We study a particular class of disclosure policies that use messages, called unbiased subhistories, consisting of the actions and rewards from a subsequence of past agents. Each subsequence is chosen ahead of time, according to a predetermined partial order on the rounds. We posit a flexible model of frequentist agent response, which we argue is plausible for this class of "order-based" disclosure policies. We measure the success of a policy by its regret, i.e., the difference, over all rounds, between the expected reward of the best action and the reward induced by the policy. A disclosure policy that reveals full history in each round risks inducing herding behavior among the agents, and typically has regret linear in the time horizon $T$. Our main result is an order-based disclosure policy that obtains regret $\tilde{O}(\sqrt{T})$. This regret is known to be optimal in the worst case over reward distributions, even absent incentives. We also exhibit simpler order-based policies with higher, but still sublinear, regret. These policies can be interpreted as dividing a sublinear number of agents into constant-sized focus groups, whose histories are then revealed to future agents

A Contract-extended Push-Pull-Clone Model

International audienceIn the push-pull-clone collaborative editing model widely used in distributed version control systems users replicate shared data, modify it and redistribute modified versions of this data without the need of a central authority. However, in this model no usage restriction mechanism is proposed to control what users can do with the data after it has been released to them. In this paper we extended the push-pull-clone model with contracts that express usage restrictions and that are checked a posteriori by users when they receive the modified data. We propose a merging algorithm that deals not only with modifications on data but also with contracts. A log-auditing protocol is used to detect users who do not respect contracts and to adjust user trust levels. Our proposed contract-based model has been implemented and evaluated by using PeerSim simulator

Using ICT tools to manage knowledge: a student perspective in determining the quality of education

Within the e-learning context of a university, technology has the potential to facilitate the knowledge interaction between the source (instructor) and the recipient (students). From a literature review, it can be concluded that prior studies have not explored the types of channels that encourage knowledge transfer in this environment. For example, how explicit knowledge travels through the e-learning environment and goes through interaction processes and is received and acquired is largely unknown. According to Alavi & Leidner (2001), Information and Communication Technology (ICT) can help speed up the processes of transferring knowledge from those who have knowledge to those seeking knowledge. Within the university context, technologies such as email, Internet, IRC chat, bulletin boards and tools such as WebCT and BlackBoard have the potential to facilitate the transfer of knowledge and act as a link between source and recipient. Effective knowledge transfer has to consider effective knowledge acquisition, which are therefore inexplicably linked. Nonaka's spiral model addresses knowledge acquisition through spiraling processes in which an individual would be able to convert tacit knowledge to explicit knowledge and vice versa. According to Nonaka & Takeuchi (1995) there are four types of interaction, which give way to the conversion of one form of knowledge into another, namely tacit-to-tacit, tacit-to-explicit, explicit-to-tacit and explicit-to-explicit. In an academic environment, this can be studied as the source, either transferring tacit or explicit knowledge, and similarly as the recipient, receiving knowledge either in tacit or explicit form. Nonaka & Takeuchi (1995) also refer to this as the SECI model, where SECI stands for Socialisation, Externalisation, Combination and Internalisation. This 'Research in Progress' reports the outcomes of a study undertaken to understand how and to what extent knowledge spiraling processes and accompanying characteristics of SECI can be ICT-enabled to contribute towards the studying and learning processes for university education. A survey instrument was developed for this purpose and it is currently undergoing peer-review and other customary validity and reliability tests. Once the instrument is validated, it will be administered on about 50 tertiary students. It is hoped that the results obtained from this survey will be reported in the QIK 2005 conference

Towards a conceptualization of casual protest participation: Parsing a case from the Save Roşia Montană campaign

There is currently an empirical gap in the literature on protest participation in liberal democracies which has overwhelmingly focused on Western Europe and North America at the expense of Eastern Europe. To contribute to closing that gap, this article reviews findings from a multi-method field study conducted at FânFest, the environmental protest festival designed to boost participation in Save Roşia Montană, the most prominent environmental campaign in Romania. By contrast to its Western counterparts, Romania has seen markedly lower levels of involvement in voluntary organizations that are a key setting for mobilization into collective action. Concurrently, experience with participation in physical protests is limited amongst Romanians. Specifically, the article probes recent indications that social network sites provide new impetus to protest participation as an instrumental means of mobilization. Dwelling on a distinction between experienced and newcomers to protest, results indicate that social network site usage may make possible the casual participation of individuals with prior protest experience who are not activists in a voluntary organization. Whilst this finding may signal a new participatory mode hinging on digitally networked communication which is beginning to be theorized, it confounds expectations pertaining to a net contribution of social network site usage to the participation of newcomers to protest