A survey-based study of 246 village-level social welfare data operators found that business architecture, cross-organizational collaboration and technological readiness were positively and statistically significantly associated with perceived e-government integration impact in two Indonesian regencies. The research, focused on Gunung Kidul and Bangkalan, examined organizational, institutional, relational and technological capabilities associated with social welfare data integration. Governance and leadership and information policy did not show statistically significant direct relationships with perceived integration impact in the pooled sample.

The authors analyzed the survey with partial least squares structural equation modeling. In their framework, e-government integration impact, or EGII, represents perceived improvements in information, system and service quality, responsiveness and public trust. E-government integration scope, or EGIS, describes the breadth of integration across data, applications, electronic communication and knowledge-sharing processes.

EGII was strongly associated with EGIS, meaning that operators reporting more favorable perceived integration outcomes also tended to report broader integration. Because this was an observational analysis of survey responses, the results establish statistical relationships rather than causal effects. The findings identify three capabilities with significant direct relationships to EGII in the pooled model: business architecture, cross-organizational collaboration and technological readiness.

The evidence does not provide operational definitions for these constructs, but the results show that their measured levels were associated with respondents’ assessments of e-government integration outcomes. By contrast, governance and leadership and information policy had no statistically significant direct relationships with EGII in that analysis. This absence of a detected direct relationship does not establish that the two factors are unimportant or that they operate only through indirect pathways, because the supplied findings do not report such indirect effects.

A regency-based robustness analysis found that some relationships were sensitive to local implementation conditions. Significant differences between Gunung Kidul and Bangkan were reported for the technological-readiness-to-EGII relationship and the EGII-to-EGIS relationship. The geographic comparison therefore cautions against assuming that the same capability relationships apply uniformly across the two regencies, although it does not identify the specific local practices responsible for those differences.

Its direct contribution is to show which measured interoperability capabilities were associated with perceived integration impact in the surveyed population, alongside evidence that some relationships varied between the two regencies.

Subject of Research: Survey-based analysis of interoperability capabilities for social welfare data integration in two Indonesian regencies

Article Title: Determinants of E-government interoperability capabilities for social welfare data integration in Gunung Kidul and Bangkalan Regencies, Indonesia

Article References: Fikri, R., Purnomo, E. P., Pribadi, U., Mohamed, A. M., & Mohammad, N. B. (2026). Determinants of E-government interoperability capabilities for social welfare data integration in Gunung Kidul and Bangkalan Regencies, Indonesia. Discover Sustainability. https://doi.org/10.1007/s43621-026-04608-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04608-5

Keywords: social welfare data, e-government, interoperability, Indonesia, PLS-SEM, technological readiness, cross-organizational collaboration, business architecture, digital governance, public services, Determinants, capabilities

From orbit, today’s space-based laser instruments measure Earth’s forests and terrain one narrow line at a time. Between those lines are enormous gaps that include millions of acres of land scientists never directly measure. NASA’s CASALS instrument will blend machine learning with an ultra-compact lidar instrument to adaptively ‘sweep’ large swaths of Earth’s surface. This new capability for space-based remote sensors allows CASALS to create truly three-dimensional (3D) topographical renderings. 

A team of researchers at NASA’s Goddard Space Flight Center (GSFC) has developed a new instrument designed to eliminate blind spots between lines of lidar data on the Earth’s surface. Built with support from NASA’s Earth Science Technology Office (ESTO), the Concurrent Artificially-intelligent Spectrometry Lidar System (CASALS) could help researchers map the features of Earth’s surface with unprecedented detail. Rather than mechanically steering a laser with moving mirrors, CASALS steers light using wavelength itself, generating a continuous, 3D swath of the ground below. 

John Yorks, a physical research scientist at GSFC and Principal Investigator for CASALS, explained that the instrument features a novel transmitter and optical grating system that manipulates laser pulses without heavy, failure-prone motorized mirrors. 

Instead, CASALS uses a fast-pulsing, tunable “seed” laser centered at 1040 nanometers, rapidly cycling through 256 unique wavelengths as it pulses through an optical component with a microscopic pattern of ridges called a transmission grating. 

At this point, the physics of diffraction takes over. Because light bends at slightly different angles depending on its exact wavelength, the grating naturally “steers” each of the 256 pulses into a distinct position across the flight path. 

This non-mechanical approach paints a continuous 3D swath of the ground. Not only does this eliminate the large unmapped gaps left between the isolated tracks of legacy spaceborne lidars, but stripping away moving parts drastically reduces the instrument’s size, weight, and power (SWaP) requirements. This makes it more reliable for the harsh environment of space. 

Another key innovation within CASALS is its detector array. This component features a uniquely high Quantum Efficiency (QE), a variable that describes how effectively a detector converts incoming light (photons) into an electrical signal. CASALS operates at 80% QE. This means CASALS wastes almost none of the light that makes it back to the sensor. 

With QE that high, CASALS can detect much weaker return signals, allowing it to collect useful measurements from darker surfaces or through denser vegetation. 

This technology isn’t limited to a laboratory environment. In November 2024, an airborne CASALS prototype flew aboard NASA’s B200 King Air aircraft. The instrument produced science-quality datasets on its first campaign, exceeding the team’s expectations. 

“I’m very impressed with Guang Yang, Jeffrey Chen, and all our engineers, and how well CASALS performed. This worked really well from the first time we flew it, which is pretty impressive,” said Yorks. 

Flying at an altitude of roughly five kilometers, CASALS can distinguish individual tree branches and subtle changes in ground elevation, thanks to its 22-centimeter horizontal resolution. 

“We’re able to see really fine details of vegetation structure and changes in the topography of the surface,” Yorks said. 

The team aims to fly CASALS again as part of the Airborne Surface, Cryosphere, Ecosystem, and Nearshore Topography (ASCENT) campaign, scheduled for summer 2027. 

But there’s more work to do to prepare the technology for space. The team plans to make several improvements to CASALS before creating a space-ready prototype. Specifically, they’re widening the instrument’s swath and adding onboard data processing software. 

Transitioning from a successful airborne prototype to a fully realized space mission is a challenge, but the CASALS team is up to the task. 

“The team’s really done a great job of sticking together and keeping on task and on schedule. We are being funded to optimize and improve the performance, and we’re also then looking at ways to take the technologies used in the airborne instrument and mature them and prepare them for a spaceborne version in the future,” said Yorks. 

If successful, CASALS could become the first spaceborne lidar capable of mapping Earth’s forests and terrain as continuous three-dimensional swaths rather than isolated profiles, giving scientists a dramatically more complete picture of our changing planet. 

For additional details, see the entry for this project on NASA TechPort. 

Project Lead(s): John Yorks, NASA’s Goddard Space Flight Center 

Sponsoring Organization(s): NASA’s Decadal Survey Incubation (DSI) program; NASA’s Instrument Incubation Program (IIP); NASA’s Goddard Space Flight Center; University of California, Santa Barbara; Virginia Polytechnic Institute and State University 

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