Evidence map›Paper›PMID 35741508›Full record

ReviewEntropy (Basel, Switzerland)2022

A Review of Technological Forecasting from the Perspective of Complex Systems.

Lijie Feng, Qinghua Wang, Jinfeng Wang, Kuo-Yi Lin

Abstract readReview
In one paragraph

Review in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lijie FengSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, China.
Qinghua WangSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, China.
Jinfeng WangChina Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.ORCID 0000-0002-3548-2674
Kuo-Yi LinSchool of Business, Guilin University of Electronic Technology, Guilin 541004, China.

Funding

Innovation Method Fund of China 2018IM020300Innovation Method Fund of China 2019IM020200Joint Funds of the National Natural Science Foundation of China U1904210-4Shanghai Science and Technology Program 20040501300
6 · The paper itself

Abstract

Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

Indexed as

complex systemsco-occurrence networkdata analysisliterature reviewtechnological forecasting

Identifiers

PMID35741508
PMCPMC9223049

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.