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Information technology (IT) capability is an organizational capability that enables organizations to acquire, deploy, combine, and reconfigure IT resources. As such, it is often investigated in conjunction with organizational agility—an organization’s ability to sense and respond to changes—and organizational performance. Studies on IT capability distinguish between reactive and proactive IT capability and identify varying effects in relation to agility and performance. While reactive IT capability supports and enhances work processes, proactive IT capability supports and enhances business strategies. In the light of the mixed results of prior research, we conduct a meta-analytical investigation into the varying effects that reactive and proactive IT capability have on organizational agility and organizational performance. We identified 6.436 studies from multiple sources that we systematically reduced to include 72 empirical studies in our analysis. Contrary to previous results and widely held opinion, our meta-analysis neither finds support for differences in effect size between reactive (r+ = 0.39, k = 34, 95% Confidence Interval (CI) [0.34, 0.44]) and proactive IT capability (r+ = 0.38, k = 21, 95% CI [0.31, 0.45]) toward agility (z = 0.68, p = 0.25), nor from reactive IT capability (r+ = 0.31, k = 43, 95% CI [0.26, 0.37]) and proactive IT capability (r+ = 0.33, k = 25, 95% CI [0.27, 0.40]) toward performance (z = 1.11, p = 0.13). Given the importance of IT capability, we discuss possible explanations and propose four areas for future research: latency, sequence, configurational, and theoretical multiplicity of IT capability.

Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste management. While these advances promise an increasingly digitalized future for collecting, sorting, and recycling waste material, little is known about the current extent of digitalization by waste management firms. Available studies focus on firms’ digitalization intentions, largely neglecting the level of actual adoption of digital technologies, and do not differentiate the level of digitalization alongside different steps of the waste management value chain. Our study reports on a cross-sectional descriptive survey that captures current digitalization efforts and strategies of 130 public and private waste management firms in Germany. We analyze their levels of digitalization along with different steps of the waste management value chain, explore their different objectives, approaches, and transformational measures with regard to digitalization. Our findings reveal that while the perceived importance of digitalization in the waste management sector continues to grow, the actual adoption of advanced digital technologies falls notably behind intentions reported in 2016 and 2017. We explore the reasons for this gap, point out so far largely ignored research opportunities, and derive recommendations for waste management firms and associations.

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection.

Data provenance, a record that describes the origins and processing of data, offers new promises in the increasingly important role of artificial intelligence (AI)-based systems in guiding human decision making. To avoid disastrous outcomes that can result from bias-laden AI systems, responsible AI builds on four important characteristics: fairness, accountability, transparency, and explainability. To stimulate further research on data provenance that enables responsible AI, this study outlines existing biases and discusses possible implementations of data provenance to mitigate them. We first review biases stemming from the data's origins and pre-processing. We then discuss the current state of practice, the challenges it presents, and corresponding recommendations to address them. We present a summary highlighting how our recommendations can help establish data provenance and thereby mitigate biases stemming from the data's origins and pre-processing to realize responsible AI-based systems. We conclude with a research agenda suggesting further research avenues.

Given the scale and scope of consumer conversations on social media, it is both possible and challenging for organizations to identify valuable ideas from those conversations that could lead to successful innovations. We examine how a large retailer developed a new capability to leverage public social media conversations for innovation through a process of filtering, assessing, converting, and deploying. By inductively developing a process model of how the retailer managed to convert consumer conversations on social media into organizational innovation, our study sheds light on the microfoundations of this important capability.

In this paper, we present experimental evidence from a ‘yes’/‘no’ judgement task and two acceptability rating studies (Experiments 1a-c) for the claim made in Hinterwimmer (2019) that sentences with two anaphorically interpreted complex demonstratives are less acceptable than sentences with two anaphorically interpreted definite descriptions and sentences where one of the two previously introduced referents is picked up by a complex demonstrative, while the other one is picked up by a definite description. The results of Experiment 1a and 1b are in principle compatible with the account argued for in Hinterwimmer (2019), according to which the (potentially abstract) demonstrations presupposed by demonstratives may not have overlapping trajectories. However, sentences with two anaphorically interpreted complex demonstratives are not judged as unacceptable as would be expected if they involved a presupposition violation. Therefore, we propose an alternative, economy-based pragmatic account that builds on Ahn (2019) and Nowak (2019). The question of whether the observed pattern is more compatible with the account proposed by Hinterwimmer (2019) or the alternative pragmatic account is directly addressed in a further acceptability rating study (Experiment 1c). The design of that study is similar to that of Experiment 1b, but it includes as fillers both sentences clearly violating a presupposition and sentences violating a pragmatic constraint. Since the ratings for sentences containing two anaphorically interpreted complex demonstratives are closer to the ratings for sentences violating a pragmatic constraint than for sentences violating a presupposition, we conclude that the alternative pragmatic account is preferable to the account by Hinterwimmer (2019).

The present paper reports two acceptability-rating experiments and a supporting corpus study for Polish that tested the acceptability and frequency of five verb classes (WATCH, SEE, HATE, KNOW, EXHIBIT), entailing different sets of agentivity features, in different syntactic constructions: a) the personal passive (e.g. zachód słońca był oglądany ‘the sunset was watched’), b) the impersonal -no/-to construction (e.g. oglądano zachód słońca ‘people/they/one watched the sunset’), and c) the personal active construction (e.g. niektórzy oglądali zachód słońca ‘some (people) watched the sunset’). We asked whether acceptability ratings would show identical acceptability clines across constructions affected by agentivity, as predicted from Dowty’s (1991) prototype account of semantic roles with feature accumulation as its central mechanism, or whether clines would vary depending on syntactic construction, as predicted from Himmelmann & Primus’ (2015) prominence account that uses feature weighting to describe role-related effects. In contrasting the applicability of these two accounts, we also investigated whether previous research findings from German replicate in Polish, thereby revealing cross-linguistic stability or variation. Our results show that the five verb classes yield different acceptability clines in all three Polish constructions and that the clines for Polish and German passives show cross-linguistic variation. This pattern cannot be explained by role prototypicality, so that the experiments provide further evidence for the prominence account of role-related effects in sentence interpretation. Moreover, our data suggest that experiencer verbs interact differently with the animacy of the subject referent, yielding different results for perception verbs (SEE), emotion verbs (HATE), and cognition verbs (KNOW).