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Improving Social Alignment During Digital Transformation

Communications of the ACM

Social alignment among groups of stakeholders occurs when different stakeholder groups share understanding of a business outcome and commit to the outcome and the means to achieve it.12 Project management is more effective and efficient when stakeholders are socially aligned because it reduces friction each time a project decision is made. Without agreement among stakeholders as to what needs to be accomplished and how to do so, success becomes harder to achieve. While the benefits of stakeholder social alignment are clear, how project stakeholders can move toward and sustain social alignment remains unknown.13 To address this issue, we sought to determine how social alignment or misalignment develops, and how leaders can improve social alignment over time. We had a unique chance to learn answers to these questions through our involvement in a longitudinal case study of a digital transformation. The case involved the launch of one of Australia's first large-scale digital hospitals, one of the most significant organizational changes ever undertaken by an Australian health service.a Given the size and consequences of transformational projects, this is a context where social alignment is likely to be critical. We found in our research that the process of achieving social misalignment involved four phases as did the process of achieving social alignment.


Misalignment Between Supply and Demand of Quality Content in Peer Production Communities

AAAI Conferences

In peer production communities, individual community members typically decide for themselves where to make contributions, often driven by factors such as “fun” or a belief that “information should be free”. However, the extent to which this bottom-up, interest-driven content production paradigm meets the needs of consumers of this content is unclear. In this paper, we introduce an analytical framework for studying the relationship between content production and consumption in peer production communities. Applying our framework to four large Wikipedia language editions, we find extensive misalignment between production and consumption in all of them. We also show that this misalignment has an enormous effect on Wikipedias readers. For example, over 1.5 billion monthly pageviews in the English Wikipedia go to articles that would be of much higher quality if editors optimally distributed their work to meet reader demand. Examining misalignment in more detail, we observe that there is an excess of high-quality content about certain specific topics, and that the majority of articles with insufficient quality are in a stable state (i.e. not breaking news). Finally, we discuss technolo- gies and community practises that can help reduce the misalignment between the supply of and demand for high-quality content in peer production communities.


Nomad Base Station Pro Wireless Charger: An AirPower Alternative

WIRED

Ever wake up to find your smartphone at zero percent, despite it staring back at you from the wireless charger on your bedside table? You likely didn't align the two perfectly before bed, which means you now need to plug your phone in with a cord to quickly get some juice before the day starts, whether you're commuting or working from home. These charging misalignments may not happen too frequently, but when they do, they are a source of annoyance and frustration. This is the state of wireless charging today (and has been for several years). These pads and stands aren't as convenient as promised--sure, you don't need to fumble around in the dark for a cord, but you still need to make sure your device is in the right position.


Bayesian Inverse Reinforcement Learning for Collective Animal Movement

arXiv.org Machine Learning

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.


Business and IT misalignment plagues majority of organizations

#artificialintelligence

A majority of enterprises think their networks are not fully ready to support their operations, with misalignment between IT and business needs cited as one of the top barriers to effective enterprise networks, according to a new report from IT services and consulting firm Accenture. The firm surveyed 300 senior IT and business executives from 10 industries and seven countries, and found that while organizations have embraced advanced digital technologies such as the Internet of Things (IoT)/edge computing (cited by 77 percent), big data/analytics (cited by 83 percent) and digital customer experience (cited by 78 percent), only 36 percent are "very satisfied" that their network is capable of supporting their business needs. The survey showed that just 36 percent of respondents are "very satisfied" with their overall capability, and 38 percent are satisfied with network bandwidth. Less than half reported being "very satisfied" with their network performance (c43 ited by percent), and half said they are satisfied with security and reliability. These levels remained largely the same when considering networks' ability to meet the needs of the business in 18 to 24 months.