Crowdsourcing (CS) of micro tasks is a relatively new, open source work form enabled by information and communication technologies. While anecdotal evidence of its benefits abounds, our understanding of the phenomenon's societal consequences remains limited. Drawing on value sensitive design (VSD), we explore microtask CS as perceived by crowd workers, revealing their values as a means of informing the design of CS platforms. Analyzing detailed narratives of 210 crowd workers participating in Amazon's Mechanical Turk (MTurk), we uncover a set of nine values they share: access, autonomy, fairness, transparency, communication, security, accountability, making an impact, and dignity. We find that these values are implicated in four crowdsourcing structures: compensation, governance, technology, and microtask. Two contrasting perceptions-empowerment and marginalization-coexist, forming a duality of microtask CS. The study contributes to the CS and VSD literatures, heightens awareness of worker marginalization in microtask CS, and offers guidelines for improving CS practice. Specifically, we offer recommendations regarding the ethical use of crowd workers (including for academic research), and call for improving MTurk platform design for greater worker empowerment.

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Information systems (IS) support in organizations has undergone dramatic changes over the years. IS professionals in the support function have become an important knowledge source to colleagues who seek assistance with their IS usage. Our understanding of IS professionals' customer‐oriented behaviours is limited, however. Focusing on IS post‐implementation support and drawing upon organizational citizenship behaviour (OCB) theory, this paper seeks to understand IS professionals' citizenship behaviours in supporting colleagues. Our analysis of 630 support tasks performed by IS professionals with regard to two systems at three periods reveals five types of customer‐oriented OCB: anticipation, education, justification, personalization‐technology and personalization‐business. Our results also show different associations between four contextual factors of IS support (i.e. system, user, task and problem) and the OCBs. In instances of user deficiency, more personalization‐business and anticipation OCBs were observed across all the four problem domains (functionality, data, workflow and role). By contrast, in instances of system deficiency, more personalization‐technology OCBs were observed among the two problem domains of data and functionality. Moreover, the occurrence of OCBs revealed a temporal pattern such that personalization‐business OCBs are more pronounced in early post‐implementation periods whereas anticipation OCBs and personalization‐technology OCBs become more dominant later. The categorization scheme of the customer‐oriented OCB, the OCB dynamics and the patterns between OCB types and the contextual factors advance our understanding of the evolving and challenging work of organizational IS support. Our findings extend the OCB literature on customer orientation and enrich the limited studies on knowledge‐intensive IS support work. Practical implications of the findings on IS management and policies are discussed.