MIS Doctoral Seminar

Summary

• Big data’s unique characteristics – Four “V”
• Big data’s implications for the information value chain
• The potential topics in three major IS research traditions (Behavior, Design, Economics of IS )

The Information Value Chain and Big Data

• People, process and technology
• Knowledge derivation and decision making
• Big data’s disruptive role: involves a different set of people, processes, and technologies; “platforms” and “pipelines”; greater reliance on data analyses

Discussion

• The paper offers many potential IS research topics, but in some of them, big data are more like “accumulation” rather than “disruption”
• Is big data an IT artifact? How about other IT artifacts, like Artificial Intelligence, is the framework still useful in terms of offering related IS research questions?

This paper addresses the need for philosophy and its relevance for IS. It gives an overview of some of the key works in the relevant disciplines of philosophy and extrapolate next steps and further research.

The authors focus on four well-established fields:

1. Metaphysics: the first philosophy
2. Epistemology: why do we think we know and the problem of epistemology in IS
3. Rationality: logic, rationality and the reasoning process
4. Axiology: what is good and to be valued, and what should we do and why should we do it?

Discussion:

• This paper opens up our minds to alternative thinking. Encourage researchers to take alternative reasoning, argumentation, and rhetorical styles into IS research.
• As the authors mention: “writing philosophy is the exact opposite of such a strategy in research for there can never be a cookie-cutter way to philosophise.” What’s the future of this alternative way? Will it be a niche in IS? It seems this approach is hard to become a mainstream in IS or at least it will take a long way to go there.

This paper focuses on the conceptualization of causality. Specifically, it discusses on theorists’ explicit or implicit assumptions about the nature, aspects, and meanings of the concept of causality as it relates to their research interests. Authors proposes a framework of causal reasoning for IS research, consisting of a set of positions on three core causal dimensions. They argue that a deeper understanding of causality will contribute to a deeper understanding of theory and thereby alleviate some of the confusion surrounding theoretical contributions.

 

Dimension I. Causal Ontology

Causal ontology concerns a theorist’s views about the reality of causality.

• Causality is a convenient metaphor for a logical or metaphysical association.
• Causality implies a real mechanism, that is, a process that connects inputs to outputs.
• Causality is a misnomer, because it incorrectly implies unidirectional, deterministic, external forces.

 

Dimension II. Causal Trajectory

Causal trajectory refers to a theorist’s views about the causal movements of an affected entity in space and time.

• Causality occurs across the boundaries of a stratified entity.
• Causality occurs within (internal to) an undifferentiated entity.
• Causality occurs through the accretion (growth and complexification) over time of a heterogeneous entity.

 

Dimension III. Causal Autonomy

Causal Autonomy refers to a theorist’s views about movement of causal effects between human (or social) actors and technology.

• Causal effects move from people (or social actors) to technology—technology as instrument.
• Causal effects move from technology to people (or social actors)—technology as influencer.
• Causal effects move back and forth between people (or social actors) and technology—technology as interactant.

 

Concerns

Authors also mention that using the framework leads to multiple combinations of the positions in the framework. However, they also mention some combinations seem incompatible or uncritical. Therefore, it seems uncertain which position, and which dimension, a researcher should depend on.

Brief summary

• Causal ontology dimension
  • Causality is a convenient metaphor for a logical or metaphysical association
  • Causality implies a real mechanism, that is, a process that connects inputs to outputs
  • Causality is a misnomer, because it incorrectly implies unidirectional, deterministic, external forces
• Causal trajectory dimension:
  • Causality occurs across the boundaries of a stratified entity
  • Causality occurs within (internal to) an undifferentiated entity
  • Causality occurs through the accretion (growth and complexification) over time of a heterogeneous entity
• Causal autonomy dimension
  • Causal effects move from people (or social actors) to technology— technology as instrument
  • Causal effects move from technology to people (or social actors)— technology as influencer
  • Causal effects move back and forth between people (or social actors) and technology—technology as interactant

Discussion:

• Interesting parts
  • Didn’t fall into the loop to find the union of disparate and irreconcilable definitions of causality, but rather discuss it in a more decent way.
  • Gives us a holistic vision of causal structure, and the first two perspectives could be generalized to other disciplines.
• Critiques:
  • Discussion from causal ontology dimension has little “IT artifact” features except the example part mentioned some IS research, and the main arguments results from philosophy and social science literature. However, this might indicate the potential to build theory in this field.

Overall Framework

• Information value chain
  • knowledge derivation (e.g., knowledge management)
  • decision making (e.g., decision support system)
• The four Vs of big data include volume, variety, velocity, and veracity
• Big data has disrupted information value chain because of large quantities of unstructured data.

Big Data and IS research

• Big data can play a role in the two kinds of information value chain in the discipline of behavioral science, design science, and economics of IS.
• Not to mention the new opportunities created by big data, the critical question pertaining to this discussion is how we can manage the tension between the relatively old way of doing research and the new way enabled by big data.

Implications of Big data for Research

• Despite boundless research opportunities through utilization of big data, the authors are cautious about big data, which they state “cautious optimism” for two reasons
  • a) In terms of theory and methodology, big data methods generally answer to “what” rather than “why”.
  • b) In terms of construct validity/credibility, we are still lack of knowledge and expertise to adequately validate variables derived from diverse methodologies and large datasets.

Discussion

• This paper basically illustrate us in detail the implication of big data on IS and business research. However, not any possible solution to the issues pertaining to big-data is presented (e.g., big data has limitation in that it does not give us answer to “why”, but only suggest “what.”)

What is IS research?

• IS field’s traditional notions of what information is about: information for decision making, automation, and effective control of organizations
• Information, in some cases, is being used to disrupt and break down decision making and create havoc within organizations rather than support them

How is philosophical work represented and perceived in the IS discourse?

• How do we inspire our research? “What is . . .?” question.
  • What is information? What is technology?
  • For IS research, these varied interpretations of metaphysics provide a rich foundation for developing the field’s own metaphysics, as the field begins to re-examine the essence of its own core concerns, including information, systems, and technology.
• Epistemology: why do we think we know and the problem of epistemology in IS

Logic, rationality, and the reasoning process

• The bulk of the reasoning process applied in IS research is deductive reasoning
• inductive reasoning

Axiology: what is good and to be valued, and what should we do and why should we do it?

• Ethics is the philosophical discipline interested in questions of right and wrong, good and bad, do’s or don’t
• Notably virtue ethics, duty ethics (deontology), and consequentialism.

Discussion

1. What is the scope of IS research? When we are asking the “what” question like “ what is information?” and “What is technology”? Do we need to discuss them together? Information (knowledge) and technology are two terms that have been investigated for several years. What is the unique aspect of IS research?
2. The author mentions that “The domain in which the research resides, whether it is quantitative or qualitative, is at best not a productive way to begin the research”, they say that starting with a focus on the problem and research questions will best serve the researcher. However, we find that researchers do choose their favorite methodology in reality.

Research Question

This paper examines how the interplay of an app’s internal architecture with its external architecture affect its performance in intra-platform competition.

Key Concepts

• Near- Decomposability:  It is based on Simon’s notion of architectural near- decomposability:  independence among and interdependence within systems. 
• Modularity of external architecture
• Monolithicity of internal architecture
• Platform synergy: The degree to which an app’s implementation leverages the focal platform’s capabilities
• Competitive lead

Empirical Study

• Blackberry and apps on the platform
• Reasonable measurements and many control variables

Discussions

• I like the idea that depicting the platform as an ecosystem and transplanting the general concept ( near- decomposability) to the app platform context.
• It remains unknown whether achieving platform synergy is costly for app developers
• In model 2, there are no control variables.
• Generalizability of the results (Blackberry context)

This paper develops a theory of how the interplay between an app’s internal and external architecture shape it ability to leverage the platform to outcompete rival apps.

Simon’s notion of architectural near-decomposability: the greater the degree to which any system comprises smaller systems that are internally interdependent but relatively independent from each other, the better it will adapt to its environment.

Research model:

The near-decomposability of an app’s architecture is characterized by:
a) Modularity of its external architecture
b) Monolithicity of its internal architecture

Discussion:

• This paper focused on both interdependence among and interdependence within systems. It’s more comprehensive than previous studies which didn’t pay attention to the internal structure of dependencies within an app.
• The authors used Likert scales for all predictors, which is too subjective. It would be better if they can include some objective data from organizations or developers.
• Blackberry shrinks its market share rapidly since 2012 and fell to 0.0 in 2016. Can the theories be generalized to all apps’ architecture? Will the hypothesis still be supported if apps are in different systems, such as Android and iOS?       

Red Queen competition—implies that an entity (such as an organization, market offering, or system) must evolve progressively faster just to keep up with its cohort of rivals.

This work sparks a conversation around why evolvable synthetic systems and the organizations that they create are increasingly inseparable from the IS discipline,  and opportunities for research where IS can play a pivotal role.

Phenomenon observed

• Strategic exploitation of IT with a goal of creating even temporary market advantages requires systems to be inherently evolvable.
• Embedded software is making nontechnology industries increasingly indistinguishable from the IT Industry
• Internet-of-Things explodes both distributedness and scale of Information Systems

Three uncharted spaces for IS research

• Designing Systems for Disassembly, Not Just Integration
  • develop a deeper understanding of the dynamic reapportionment of an IT system between apps and infrastructure over time
  • how the technological structure of a system enables and constrains organizational adaptation, and into its interplay with previously studied organizational properties
• Architecture Is the DNA of Systems, Unchangeable Yet Preordaining
  • Architecture and modularity
  • Real options thinking
• Inferring Systems Evolvability
  • Long-term and short-term
  • Operational and Strategic

Discussions

1. This work started the article by discussing some phenomenon, but the suggestions to IS researchers are mainly around platforms. However, the evolvable systems are not only about apps and platforms. For example, when nonIT industry is trying to incorporate IT into the existing structure, what challenges will be for the evolvable system?
2. The “ephemeral competitive advantage” is not clearly explained in this article.
3. A suggestion to the research areas that IS could examine is the interaction between nonIT and IT industries.

Briefly Summary:

This paper mainly gave researchers some guidelines about potential research we could do regarding the evolvability of IS.

Evolvable systems competitively matter more today because interminable systems can straitjacket organizations facing ephemeral competitive advantage, non-IT (information technology) industries are acquiring properties of the IT industry, and the IoT is increasing the scale and distributedness of systems.

These phenomena collectively open up three uncharted spaces for IS research, all focused on the “system” as the unit of analysis. These include designing systems for disassembly rather than just integration; architecture as a mass-coordination device; and approaches for inferring systems evolvability personalized to IS research conversations.

Discussion

• Interesting parts
  • Give us some new ideas and could help us fully understand the information systems. The first new idea is about the evolvability. This paper emphasized on the evolvability of the system and related uncharted theoretical and empirical territory for IS research. The second one is about the disassembly. This paper shifted our focus from “plug and play” to “unplug and play”.
• Critiques
  • Take internet of thing as one of the reason is quite far away from reality. The foundation of IoT—5th generation network remains unknown four years ago, which the paper is published. And even if the 5G technology is fully developed and implemented in China, it’s a bit early for America to talk about explosion of IoT if they continue to forbid Huawei’s technology and want to develop 5G network themselves. At least it will take 2-3 years (which Huawei takes).
  • The evolvability topic might not fit organizations with small size. And for large organizations, evolvability might cause maladaptive consequences similar like ERP.