ranks_antifragility: Antifragility Data (complete rankings with covariates)

In MSmix: Finite Mixtures of Mallows Models with Spearman Distance for Full and Partial Rankings

Antifragility Data (complete rankings with covariates)

Description

The Antifragility dataset came up from an on-line survey conducted during spring 2021 by Sapienza University of Rome in collaboration with the Italian incubator Digital Magics, to investigate the construct of antifragility in innovative startups. Antifragility reflects the capacity of a company to adapt and improve its activity in the case of stresses, volatility and disorders triggered by critical and unexpected events, such as the COVID-19 outbreak which motivated the survey. On the basis of their experience and knowledge, a sample of N=99 startups provided their complete rankings of n=7 desirable antifragility properties in order of importance. The antifragility features are: 1 = Absorption, 2 = Redundancy, 3 = Small stressors, 4 = Non-monotonicity, 5 = Requisite variety, 6 = Emergence and 7 = Uncoupling.

Usage

data(ranks_antifragility)

Format

A data frame gathering N=99 complete rankings of the n=7 antifragility features in each row (rank 1 = most preferred item). The definition of the antifragility aspects is detailed below:

Absorption

Ability to absorb stress and shocks while remaining in the planned state.

Redundancy

Overcapacity to defend from risks and prevent faults.

Small_stressors

Ability to exert low levels of stress on the organization.

Non_monotonicity

Capacity to learn from failures and errors.

Requisite_variety

Need for regulatory agents (i.e., government agency) to monitor and control organization’s outcomes and behaviors.

Emergence

Existence of cause-effect relationships between organization’s activity at micro level and its outcomes at macro level.

Uncoupling

Existence of strong interconnection between agents inside and outside the organization.

Industry_sector

Industry sector of the startup.

Market

Market type in which the startup operates.

Innovation_type

Main innovation type of the startup.

Approach_to_crisis

Main approach implemented by the startup during Covid-19 outbreak.

Crisis_impact

Impact of Covid-19 outbreak on the startup.

Age

Age of the startup (years).

N_employees

Number of employees in the startup.

Region

Italian region of the startup.

Job_title

Job title of the startup participant in the survey.

Experience

Years of job experience of the startup participant in the survey.

References

Ghasemi A and Alizadeh M (2017). Evaluating organizational antifragility via fuzzy logic. The case of an Iranian company producing banknotes and security paper. Operations research and decisions, 27(2), pages 21–43, DOI: 10.5277/ord170202.

Examples

str(ranks_antifragility)
head(ranks_antifragility)

MSmix documentation built on April 3, 2025, 9:29 p.m.