AI Capabilities Scale

Abstract

The AI Capabilities Scale (AICAP) (Abou-Foul, Ruiz-Alba, & López-Tenorio, 2023) was developed to assess artificial intelligence (AI) capabilities by broadening the definition to encompass various facets of business model innovation, information management, and computer science. This instrument is designed to measure the capabilities of AI-based technologies within organizational contexts. The scale was developed as part of a larger measurement model and evaluated using a sample of respondents from manufacturing firms located in the United States and the European Union. The construction of the 17-item AICAP scale followed the C-OAR-SE method (Rossiter, 2002), which includes construct definition, object classification, attribute classification, rater identification, scale formation, and enumeration. The development process involved rigorous evaluation through factor analysis, reliability assessments, and validity checks. The authors suggest that further testing of the scale is warranted in companies operating within ecosystems that might hinder the internal development of AI capabilities.

Keywords

AI Customer Value Proposition; AI Key Processes Optimization; AI Key Resources Optimization; AI Societal Good; Artificial Intelligence Capabilities; Companies; Organizations.

Authors

Abou-Foul, Mohamad; Ruiz-Alba, Jose L.; López-Tenorio, Pablo J.

Purpose

The primary purpose of the AICAP scale is to assess artificial intelligence (AI) capability by expanding the definition to include diverse applications within business model innovation, information management, and computer science.

Validity

Convergent Validity: The scale demonstrated a satisfactory level of average variance extracted (AVE), which exceeded the threshold of 0.50, providing evidence of convergent validity as per Bagozzi and Yi (1988).

Discriminant Validity: All average correlations were found to be below the cutoff value of 0.85, indicating sufficient evidence of discriminant validity as suggested by Henseler et al. (2015).

Reliability

Internal Consistency: All sub-dimensions of the AICAP scale exhibited satisfactory levels of internal consistency, with Cronbach’s alpha (𝛼) values greater than 0.70.

Factor Analysis

Exploratory Factor Analysis (EFA): EFA was conducted to investigate the multidimensionality of the AI capabilities construct. The analysis utilized principal axis factoring and ProMax oblique rotation, with eigenvalues greater than 1.0. The EFA revealed four main dimensions, which supported the theoretical conceptualization of the construct. All items, with the exception of three, loaded cleanly onto their corresponding factors, with loadings exceeding the cutoff value of 0.6 for a newly developed scale, and no significant cross-loading was reported (Kline, 2014).

Confirmatory Factor Analysis (CFA): In the CFA stage, a single-factor CFA indicated that three items were deemed unsatisfactory due to high modification indices and were subsequently removed from the final item set (Bagozzi & Yi, 1988). This led to the unidimensional model for each subscale exhibiting acceptable goodness-of-fit indices.

Instrument: AICAP Scale

Test Type: Original

Format: All 17 items of the AICAP scale are measured using a 5-point Likert scale. The response options are: 1 = Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Strongly agree.

Language Available: English

Population Group: Human (Male and Female)

Age Group: Adulthood (18 years and older)

Population Details: The scale was evaluated using respondents from manufacturing firms located in the United States.

Test Methodology: The methodology involved Test Validity (including Convergent Validity and Discriminant Validity), Test Reliability (including Internal Consistency), and Factor Analysis (including Confirmatory Factor Analysis and Exploratory Factor Analysis).

Authors Including

Author ORCID Identifier:

• López-Tenorio, Pablo J.: 0000-0003-4601-0733

Affiliation Email Addresses:

• Mohamad Abou-Foul: [email protected] (Al-Azhar University)

• Jose L. Ruiz-Alba: [email protected] (University of Westminster)

• Pablo J. López-Tenorio: No data is Available (UNIE Universidad, Facultad de Ciencias Sociales Aplicadas y de la Comunicación)

Correspondence Address:

Mohamad Abou-Foul: Al-Azhar University, Jamal Abdl, Naser St, Gaza, Palestinian Territory, Occupied, [email protected]

Permissions & Fee and Test Year

Permissions: May use for Research/Teaching

Fee: No

Test Year: 2023

Files

No file is available.

References

Abou-Foul, M., Ruiz-Alba, J. L., & López-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research, 157, Article 113609. doi.org/10.1016/j.jbusres.2022.113609

Items of the AICAP Scale

The AICAP scale consists of 17 items. No data is available for the full list of items.

Subscales: The measure includes the following subscales:

• AI customer value proposition

• AI key processes optimization

• AI key resources optimization

• AI societal good

Items

AI Customer Value Proposition

• Our company is collecting after-sales insights and uses AI to personalize the customer experience and ensure our customers’ success.

• Our specialized data science team uses tools to calculate our customer’s optimal warranty cost and duration.

• Our company is using machine learning models in pricing and quoting optimization.

• Our company collects and analyzes embedded sensor data to provide our customers with predictive maintenance and operation optimization services.

AI Key Processes Optimization

• Our company is using advanced data science in demand forecasting and stocking.

• Our company is making a strategic data acquisition to fulfill customers’ orders on time.

• Our company integrates AI conversational agents’ capabilities such as chatbots in our next-generation CRM.

• Our company uses advanced robotics and predictive maintenance in our internal operations applications.

• Our company uses intelligence capabilities such as machine vision and edge analytics in enhancing yield optimization.

• Our company uses AI data mining capabilities and big data systems to enhance our product innovation process and bill of material (BOM).

AI Key Resources Optimization

• Our company applies analytics to unified data warehouses to optimize our suppliers’ network.

• Our company uses AI applications to optimize our labor workforce.

• Our company uses advanced analytics to optimize our network’s resources, ensure cybersecurity, and safeguard our data.

• Our company uses AI applications to identify our lowest-cost provider.

AI Societal Good

• Our company trains AI assistants to enhance workplace safety.

• Our company uses applied AI such as deep reinforcement learning to cut our operation’s energy consumption, emission, waste, and equity.

• Our company uses data analytics and benchmarks to provide green solutions to our customers that tackle the most prominent societal challenges such as decarbonization.

Note: Items are rated from 1 = Strongly disagree to 5 = Strongly agree.