The job of a revenue manager is a classic example of a digital decision-making (DDM) problem because it involves the use of complex information and communication technologies (ICT) to make informed decisions that significantly impact the organization. Here’s how it aligns with the principles of DDM:

• Data Collection and Analysis: Revenue managers collect vast amounts of data from various sources, including historical booking data, market trends, competitor pricing, and customer behavior. This data is then analyzed to identify patterns and trends that inform pricing and inventory decisions.
• Modeling and Forecasting: Using sophisticated algorithms and models, revenue managers forecast future demand and set optimal prices. This involves modeling human behavior (e.g., booking patterns) and system-generated data (e.g., market conditions) to predict outcomes and make strategic decisions.
• Human-Machine Cooperation: Revenue management systems (RMS) leverage both human expertise and machine intelligence. While automated systems provide real-time data and pricing recommendations, human revenue managers interpret this information, make final decisions, and adjust strategies as needed. This cooperation ensures that decisions are both data-driven and contextually appropriate.
• Optimization and Implementation: Revenue managers continuously optimize pricing and inventory strategies to maximize revenue and occupancy rates. This involves filtering, segmenting, and augmenting information to refine models and improve decision-making processes. The goal is to devise techniques that support human-driven tasks (e.g., setting prices) and design systems that can autonomously adjust prices based on predefined criteria.
• Impact on Organizations and Society: Effective revenue management promotes significant changes within the organization by improving financial performance, enhancing customer satisfaction, and maintaining competitive advantage. These changes can also have broader societal impacts, such as influencing market dynamics and consumer behavior.

In summary, the role of a revenue manager exemplifies digital decision-making by integrating data collection, analysis, modeling, human-machine cooperation, and optimization to make strategic decisions that drive organizational success.

Note on Software and Human Machine Cooperation:

Revenue Management Systems (RMS) are specialized software tools designed to help businesses optimize their pricing strategies, manage inventory, and maximize revenue. These systems use data analysis, market trends, and forecasting to make informed decisions about pricing and availability. For short-term rental businesses, RMS can be particularly valuable in adjusting rates dynamically based on demand, seasonality, and other factors.

Examples of RMS for Short-Term Rentals

1. Beyond Pricing: This tool offers dynamic pricing solutions that automatically adjust rental rates based on market demand and other factors.
2. PriceLabs: Known for its user-friendly interface, PriceLabs provides dynamic pricing and revenue management solutions tailored for short-term rentals.
3. Wheelhouse: Wheelhouse offers advanced pricing algorithms and analytics to help property managers optimize their revenue.
4. AirDNA: While primarily a market data provider, AirDNA also offers tools for dynamic pricing and revenue management.
5. Lodgify: This all-in-one vacation rental software includes revenue management features to help optimize pricing and increase bookings.
6. Hostfully: Hostfully integrates with various RMS tools and provides dynamic pricing options to maximize revenue.
7. Rentals United: This platform offers channel management and revenue optimization tools for short-term rental businesses.
8. Guesty: Guesty provides a comprehensive property management solution with built-in revenue management features.

These RMS tools help short-term rental businesses, and their revenue managers stay competitive by ensuring their pricing strategies are aligned with market conditions and demand trends. Consequently, by leveraging these systems, property managers can increase occupancy rates and overall profitability.

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• User agency: The self-perceived abilities of users to operate and control their devices.
• Device agency: The capacities that users attribute to their smart devices.

The study introduces four agency profiles that characterize different types of human-smart device relationships:

1. Controller: High user agency, low device agency
2. Collaborator: High user agency, high device agency
3. Victim: Low user agency, high device agency
4. Detached: Low user agency, low device agency

Figure below presents an intuitive idea of the research:

The researcher is validated by conducting an exploratory factorial analysis, tag cloud analysis, and sentiment analysis on survey data to investigate these agency profiles. In this work we examined also how various background variables (such as age, gender, education, and device type) relate to user and device agency.

Key findings include:

1. The type of smart device significantly affected user agency, with household cleaning device users reporting the highest user agency and personal assistant device users reporting the lowest.
2. Most background variables (age, gender, education, professional field, work experience) did not significantly impact user or device agency.
3. The tag cloud analysis revealed distinct patterns of language use associated with each agency profile, providing insights into how users perceive their relationships with smart devices.

This research contributes to the fields of Human-Computer Interaction (HCI) and Information Systems (IS) by offering a framework for understanding the varied ways humans interact with and perceive smart devices. The findings have implications for designing more effective and user-friendly smart device interfaces and experiences. The impact of this paper on human-smart device relationships and related fields includes:

1. Theoretical contribution: The study introduces a novel framework for understanding human-smart device relationships through the lens of user and device agency. This conceptualization provides a new perspective for researchers in Human-Computer Interaction (HCI) and Information Systems (IS).
2. Design implications: The findings can inform the design of smart devices and their interfaces. By understanding different agency profiles, designers can create more user-friendly and effective devices that cater to various user perceptions and needs.
3. User experience enhancement: The research helps in identifying factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to improve overall user experience.
4. Industry relevance: The study’s insights are valuable for companies developing and marketing smart devices. Understanding user perceptions can guide product development and marketing strategies.
5. Educational impact: The research provides a foundation for educating users about their relationship with smart devices, potentially leading to more informed and empowered consumers.
6. Future research directions: The paper opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.
7. Societal implications: By shedding light on how people perceive and interact with smart devices, the study contributes to broader discussions about the role of technology in society and its impact on human behavior.
8. Methodological contribution: The combination of exploratory factorial analysis, tag cloud analysis, and sentiment analysis provides a multi-faceted approach to studying human-smart device relationships, which can be applied in future studies.

This research has the potential to influence how we design, use, and think about smart devices, ultimately advancing our understanding the human-technology interactions. Overall, you should read this paper for several compelling reasons:

• Novel perspective: The paper introduces a unique framework for understanding human-smart device relationships through the concepts of user agency and device agency. This approach offers a fresh perspective on how people interact with and perceive their smart devices.
• Practical implications and industry relevance: The research has direct applications for designers and developers of smart devices. By understanding different agency profiles, they can create more user-friendly and effective products that cater to various user perceptions and needs. Thus facilitating the development and marketing of smart devices.
• Insight into user experience: The study provides valuable insights into factors that influence user satisfaction and engagement with smart devices. This knowledge can be applied to enhance overall user experience across different types of smart devices.
• Societal impact: The research contributes to broader discussions about the role of technology in society and its impact on human behavior. It helps us understand how people perceive and interact with increasingly prevalent smart devices.
• Methodological innovation: The paper employs a multi-faceted approach combining exploratory factorial analysis, tag cloud analysis, and sentiment analysis. This methodology provides a comprehensive understanding of human-smart device relationships and could be applied in future studies.
• Future research directions: The study opens up new avenues for research in human-smart device interactions, encouraging further exploration of agency concepts in technology adoption and use.

In summary, by reading this paper, you’ll gain a deeper understanding of the complex relationships between humans and smart devices, which is increasingly relevant in our technology-driven world. The insights provided can be valuable whether you’re a researcher, designer, industry professional, or simply interested in how technology shapes our daily lives.

Link to the paper: https://doi.org/10.3390/fi16030090

Dataset for the study of Human and Device Agency

The relationship we have with our smart devices is more complex than we think! This research reveals how user and device agency influence our interactions. #TechResearch #HCI #IoT #AI
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Human-computer interaction (HCI) is a multidisciplinary field that explores the interaction between humans and computers, emphasizing especially the design and use of computer technology. Within this domain, the notion of sense of agency, usually defined as the users perceiving their actions as influencing the system, is of crucial importance. Another central notion is that of socio-technical systems, referring to the interplay between social and technical elements. In this work, we delve into users’ perceptions of their interactions with smart devices, drawing insights from a dataset collected during the COVID-19 outbreak in 2020.

The dataset, gathered through an expansive survey, was distributed in professional and personal networks, offers a window into the perspectives of 587 respondents, predominantly highly educated professionals with significant technological familiarity. While the sample’s specificity limits the generalizability of findings, it offers valuable insights into how tech-savvy users perceive their interactions with smart devices, illuminating the nuanced dynamics of HCI within socio-technical contexts.

One of the survey’s notable aspects was its focus on users’ choice of a single smart device to consider throughout their responses. This deliberate framing encourages a deeper exploration of users’ relationships with specific technologies, shedding light on the intricacies of their interactions. For respondents who did not own multiple smart devices, the instruction was to choose their primary device, such as a computer or mobile phone, thus ensuring consistency in the examination of user experiences.

Within the dataset, various dimensions of users’ interactions with smart devices emerged, encompassing cognitive, emotional, and behavioral aspects. Users’ perceptions of agency, defined as their sense of control and influence over the device, are particularly intriguing. Analysis of the dataset reveals diverse manifestations of agency, ranging from feelings of empowerment and efficiency to frustrations stemming from perceived limitations or complexities in device operation.

Moreover, the socio-technical lens allows us to delve deeper into the contextual factors shaping users’ interactions. The dataset captures not only users’ individual experiences but also the broader socio-cultural and technological landscapes influencing these interactions. Factors such as educational background, professional expertise, age, and gender are among the variables examined, offering insights into how these socio-demographic factors intersect with users’ perceptions of smart device interactions.

The dataset’s utilization in previous research endeavors underscores its significance in advancing our understanding of human-computer interaction, user behavior analysis, and agency. By building upon existing research and leveraging the data available, researchers can uncover deeper insights into the complexities of smart device interactions and contribute to the development of more user-centered technologies.

The COVID-19 context during which the data were collected adds another layer of complexity to the analysis. Due to the increased time spent at home and the increase in remote work, the global pandemic catalyzed increased reliance on technology for work, education, and socialization. Understanding users’ interactions with smart devices within this unique context provides valuable implications for designing technologies that accommodate evolving user needs and preferences.

In conclusion, users’ notions of their interactions with smart devices offer a rich tapestry of insights that intersect with key concepts in HCI, including agency and socio-technical systems. Through the analysis of a dataset collected during the COVID-19 pandemic, we gain valuable insights into users’ perceptions, behaviors, and experiences with smart devices within the context of a rapidly evolving technological landscape. This dataset serves as a springboard for further exploration in the field, driving forward our understanding of human-computer interactions in an increasingly digital world.

Where to find more:

• Agency in Human-Smart Device Relationship: A work in progress description of a research done with this dataset

Initial call for collecting the responses

Link to the dataset (Open Access)

https://doi.org/10.3390/data9040056

https://doi.org/10.34894/traony

Main Personal Related Publication

https://doi.org/10.3390/fi16030090

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Abstract:
With technology in reach of everyone and the technology sector in ascendance, it is central to investigate the relationship people have with their devices. We use the concept of agency to capture aspects of user’s sense of mastery and control in relation
to their device. This study gives preliminary evidence of the existence of two dimensions of agency for modelling the interaction between humans and smart devices: (i) user agency and (ii) device agency. These constructs emerged from an exploratory factorial analysis conducted on a survey data collected from 587 participants. Mapping the users’ dynamics with their devices into user agency and device agency advances our understanding of how digital technology shapes human cognition and emotion and how
users interact with technology. This approach can assist in designing interfaces that match the beliefs users have regarding their capabilities and the capacities of their devices.

Key words: Agency, Human-Computer Interaction, smart devices, user agency, device agency, exploratory factorial analysis, survey.

we show that agency in human-smart device interactions is a twofold and graded construct, consisting of user agency and device agency. The attributing of agency to themselves and to their device frames how the users are perceiving and leveraging the affordances of the smart device. The attribution of user and device agency can be used as a measure for understanding if the user masters the device’s potentials to the fullest or not. Thus, user agency and device agency can be used as proxies for understanding how users experience their relationships with their devices.

The conceptualization of agency proposed here should be further refined in relation to other existing constructs. We should investigate whether user and device agency act as mediators for other factors of human-device interaction such as anthropomorphism and other concepts mentioned in this article. Furthermore, our exploratory study should be developed further to a point where we can perform confirmatory factor analysis. As the next step, an expert panel will be summoned to review the items used in this study, in preparation for refining the survey before collecting new data for the second set of statistical analyses. Furthermore, we should continue exploring the nuances of agency in different contexts of smart device use, such as work vs. personal.
Brad Pitt as Tyler Durden in Fight Club (1999) states that “the things you own end up owning you”. In the context of smart devices, this is true only if we are unaware of the agency we attribute to our devices and to ourselves. Perhaps the secret of a balanced relationship with our gadgets is in making an aware decision of when and how to be “owned” by the device and when not.

Full article available at this link.

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Your help in sharing the idea is very welcome!

Agency in Human-Smart Device Relationships: An Exploratory Study #HCI #IoT #smarthome #homeautomation #survey #softwaredevelopment #softwareengineering
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Cite this paper as:

Toivonen, Heidi and Lelli, Francesco, “Agency in Human-Smart Device Relationship: an Exploratory Study” (2021). PACIS 2021 Proceedings. 145. https://aisel.aisnet.org/pacis2021/145

Check out the Related Dataset: Lelli, Francesco; Toivonen, Heidi, 2021, “A Survey for investigating human and smart devices relationships“,  https://doi.org/10.34894/TRAONY, DataverseNL, V1

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