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The aim of this paper is to describe procedural characteristics of the 4th technology foresight (TF) using search engines to discover emerging issues; analytic framework development to discover future needs; future technologies considering future needs as well as technology development; detailed description of future technology; analytical discussions of Delphi survey results; developing spatial-specific scenarios and illustrations; and examining possible adverse effects of future technologies. Korea performs TF every 5 years to establish science and technology policy and strategies. In the 4th TF, future technologies that might be developed by 2035 were discovered and Delphi survey was conducted to examine current development status, anticipated times of technology development and public use, plans to secure these technologies, etc.

This paper divides procedure employed in the 4th TF into three steps and explains seven characteristics related to its procedure.

Improvement of the TF procedure will increase the reliability and applicability of its results.

This paper consists of original results which include improved procedure and its implication by researchers who participated in the 4th TF. It will provide a useful example for other nations, hoping to introduce TF to set up national science and technology policy.

The purpose of this paper is to propose and evaluate a new matching sample comparison method, the industry size peer matching method.

Based on archival financial data from Compustat and econometric methods, the paper first validates that such a method will result in firms being divided into more homogenous groups, making peer‐performance comparison more meaningful. Then it compares this new peer matching method with previous methods through two resource‐based related studies in the IT valuation context.

The results show that the industry size matching method is a better method because: it is theoretically grounded, addressing industry, size, and random shock effects and, at the same time, avoids the selection bias caused by using a single firm as benchmark; and empirically such a technique results in more homogeneous groups and can explain more firm‐level returns than the industry‐only classification.

Matched sample comparison group analysis is widely used in both academy and industry. The paper's theoretically grounds and empirically validated matching sample comparison method provides researchers and practitioners with a tool for their future research, performance evaluation, earning management detection, or compensation contract design, when selecting the right peers is called for.