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Whenever social injustice tackled by social movements receives heightened media attention, charitable crowdfunding platforms offer an opportunity to proactively advocate for equality by donating money to affected people. This research examines how the Black Lives Matter movement and the associated social protest cycle after the death of George Floyd have influenced donation behavior for campaigns with a personal goal and those with a societal goal supporting the black community.

This paper follows a quantitative research approach by applying a quasi-experimental research design on a GoFundMe dataset. In total, 67,905 campaigns and 1,362,499 individual donations were analyzed.

We uncover a rise in donations for campaigns supporting the black community, which lasts substantially longer for campaigns with a societal than with a personal funding goal. Informed by construal level theory, we attribute this heterogeneity to changes in the level of abstractness of the problems that social movements aim to tackle.

This research advances the knowledge of individual donation behavior in charitable crowdfunding. Our results highlight the important role that charitable crowdfunding campaigns play in promoting social justice and anti-discrimination as part of social protest cycles.

The purpose of this paper is to identify strategic options and challenges that arise when an industrial firm moves from providing smart service toward providing a platform.

This conceptual study takes on a multidisciplinary research perspective that integrates concepts, theories and insights from service management and marketing, information systems and platform economics.

The paper outlines three platform types – smart data platform, smart product platform and matching platform – as strategic options for firms that wish to evolve from smart service providers to platform providers.

Investigating smart service platforms calls for launching interdisciplinary research initiatives. Promising research avenues are outlined to span boundaries that separate different research disciplines today.

Managing a successful transition from providing smart service toward providing a platform requires making significant investments in IT, platform-related capabilities and skills, as well as implement new approaches toward relationship management and brand-building.

The findings described in this paper are valuable to researchers in multiple disciplines seeking to develop and to justify theory related to platforms in industrial scenarios.

The purpose of this paper is to present a model that retailers engaged in e‐commerce (e‐tailers) can use for determining the optimal mix of customer segments within a customer portfolio from an integrated risk and return perspective.

Portfolio Selection Theory of Markowitz is applied to find the optimal composition of customer portfolios. The model is developed and discussed for two customer segments (relationship‐ and transaction‐oriented customers) and exemplarily applied to a data set of an e‐tailer.

Portfolio Selection Theory of Markowitz is well‐suited and promising for determining an optimal customer portfolio from a risk‐return perspective. However, since customers vary from financial assets in several aspects, the results of the model have to be interpreted conscientiously and the resulting action options have to be interpreted within the context of customer relationship management (CRM).

The model proposes to carry out a sequential set of one‐period optimizations. To reduce complexity, several simplifying assumptions were made within the model regarding the characteristics of customer segments and portfolio as well as the expected risk and return.

A current survey among German companies indicates that companies already have broad experiences in customer evaluation. However, it also turned out that evaluating customers' potential and risk simultaneously is still a major challenge. Our new approach facilitates the making of sound investment decisions into single customer relationships with respect to an overall optimal customer portfolio. Thus, a formal link to value‐based management is established.

Using CRM for a value‐based management of customer portfolio according to a superordinated risk management objective has so far received little attention in literature. This paper's model is a new approach in customer portfolio management for e‐tailers taking customers' risk and return characteristics simultaneously and in real‐time into consideration.

Cloud computing, a dominant technology, significantly impacts organizations, necessitating talent management strategies for sustained growth. This study aims to explore the impact of cloud adoption on large French organizations through a “learning organization” perspective.

Interviews were conducted with business and IT stakeholders from 35 multinational organizations in France.

Cloud services have a high impact on large organizations, leading to a demand for cloud-related skills, a power shift from IT to business departments and increased shadow IT activities. Effective utilization requires organizational learning and a change management project, transforming organizations into productive and innovative learning organizations.

This paper contributes to cloud computing, organizational learning and talent management literature, offering managers a novel approach to handling cloud services.

To provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.

A 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).

The findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.

This study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.