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JU Davis College of Business and Technology Marketing Professors’ Novel Research Discovers Food Ordering Apps Influence Brand Loyalty

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2 years ago

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JACKSONVILLE, Fla., Oct. 23, 2024 /PRNewswire/ — We’ve all had days where we don’t feel like going out for dinner, so we whip out our cell phones and place an order on DoorDash or some other third-party food app, an online food delivery industry projected at $1.02 trillion last year alone.

Two Jacksonville University Davis College of Business and Technology marketing professors, Drs. Irina Toteva and Selen Savas-Hall, uncovered in their new research that consumers will feel a stronger connection to a company or a brand if they put effort into creating something with that company or brand through a third-party app.

The marketing duo recently had their research, “Perceived Effort in the Co-Creation of Electronic Services and Influence on Brand Loyalty: The Case of Food Ordering Apps” published in Services Marketing Quarterly.

“This is the first research to investigate the role of perceived effort in electronic services and the influence on brand loyalty,” said Toteva, a JU assistant professor of marketing. “Our research explains about the mechanism of brand loyalty, which is in part due to the labor-to-love effect, where effort translates into attachment to the item.”

Another interesting finding of this study is that the risk that is involved in using a technology may also be seen as effort. The Davis College professors showed that when consumers perceive higher risk using a third-party app, they show higher brand loyalty to the service provider.

Perceived risk refers to the uncertainty or potential negative outcomes associated with buying and using a product or service. In the case of food-ordering apps, consumers may view the use of technology as risky, fearing issues like privacy breaches or service failures.

“Our study finds that perceived risk can actually enhance brand loyalty,” shared Toteva. “When consumers take on this risk and it turns out positively (e.g., the food is delivered successfully), they feel more loyal to the brand as a result of overcoming that uncertainty. This risk mitigation further solidifies their attachment to the brand.”

Toteva got the idea for the research when she was scrolling through Instagram on her phone.

“I clicked on a promoted post that took me to the sales page of a clothing brand. I started adding some items to my shopping cart, and it occurred to me that this was an effort on my part—I was using my time and energy to evaluate the items and decide if I wanted to add any of them to my cart,” she recalled. “I became curious if this labor-to-love effect could be applicable to consumers who use apps for the ordering of products or services like food.”

The study explores how consumers who order food on an app (such as Uber Eats or Door Dash) perceive the level of effort they contribute to the order and how that perceived effort influences their attachment to the restaurant provider.

“We show that those consumers who perceive they have made an effort into ordering their food are more likely to feel they are continuing to build a relationship with the restaurant provider, and as a result they are more brand loyal to that service provider, compared to consumers who perceive less of an effort,” said Savas-Hall, a JU associate professor of marketing and international business.

While the use of technology (such as food-ordering apps) in the service ordering process can be perceived as automatic and effortless, some consumers perceive the process as effortful, and consequently, they are more aware that they are co-creating the service with the restaurant provider.

“When consumers feel like they are co-creating the service with the food service provider, this shows their willingness to invest resources into their relationship with the food service provider,” explained Toteva. “At the same time, by using the app and putting effort into the order, consumers feel improved self-efficacy (i.e., their confidence in their ability to perform tasks) as they are able to reach their goals through effort, compared to consumers who do not perceive effort in the ordering process.”

The findings of this research are important to consumers and to service industries, such as food, groceries and fashion, where companies co-create various offerings with their customers.

“This study is applicable to any company that provides technology tools to its customers to assist them in the service ordering process. For example, scheduling a service through an app such as a haircut appointment or a fitness class, or using a food-ordering app to order products or using a rideshare app for transportation are all efforts on the side of consumers,” said Savas-Hall.

This study shows that if consumers are made aware that they contribute to the service, then this perceived effort can turn into a ‘love’ and later into brand loyalty to the service provider. As a result, emphasizing the effort made by consumers may be well-received as a positive reinforcement and may create a special connection to the service provider. The co-researchers say this study also shows that the effort does not need to be entirely physical, and that mental effort also counts.

The JU Davis College of Business and Technology is the only triple-accredited private college in all of North Florida and South Georgia, with AACSB, ABET and AABI accredited programs. Its mission is to empower students to achieve sustainable career success with a high quality, relevant and applied educational experience that is delivered by faculty committed to advancing the individual development of each student.

Media Contact:
Joanna Norris, PR/Marketing Director
(904) 534-6926
Jnorris11@ju.edu

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SOURCE Jacksonville University

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP, on the Model You Choose

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38 minutes ago

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July 24, 2026

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data staying inside their own network

LONDON, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that must keep operational data within a defined boundary, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model itself can run entirely inside your own network.”

“IT teams across the United Kingdom and EMEA tell us the same thing: they want the benefit of AI without losing sight of where their data goes,” said Greg McLaughlin, Account Executive for EMEA at JAMS Software. “JAX and JAMS MCP let them keep operational data inside their own network and still get answers in plain language. That combination is what makes this practical for the teams I work with.”

JAX and JAMS MCP are available now to all JAMS Web customers across the United Kingdom and EMEA, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software

Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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Video: CNPC offers green chemical answer

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38 minutes ago

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July 24, 2026

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BEIJING, July 24, 2026 /PRNewswire/ — A news report from chinadaily.com.cn:

Located on the edge of the Taklamakan Desert in Northwest China’s Xinjiang Uygur autonomous region, the Tarim 1.2 MTA Phase II Ethylene Project and its supporting green and low-carbon demonstration facility of PetroChina Dushanzi Petrochemical Company, a subsidiary of China National Petroleum Corporation, are offering a new example of China’s low-carbon industrial transformation.

Watch the video to discover how CNPC is exploring a cleaner and more circular future for the industry.

View original content to download multimedia:https://www.prnewswire.com/apac/news-releases/video-cnpc-offers-green-chemical-answer-302834036.html

SOURCE chinadaily.com.cn

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Shanghai Electric showcases embodied intelligence robot matrix and AI-native smart factory solutions at WAIC 2026

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38 minutes ago

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July 24, 2026

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Featuring humanoid robots with 41 degrees of freedom, pipe‑inspection robots with ±1mm positioning accuracy, and 51 industrial‑grade AI agents

SHANGHAI, July 24, 2026 /PRNewswire/ — Operations in high-end equipment manufacturing often involve confined spaces, complex objects, and fine manipulation tasks that demand sustained and stable precision. At the recent 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance (WAIC 2026), Shanghai Electric (SEHK: 02727, SSE: 601727) showcased its comprehensive portfolio of embodied intelligence solutions tailored to a range of industrial scenarios.

Themed “AI for All: Smart Squad, Shining Without Limits,” Shanghai Electric highlighted its capabilities across embodied AI robots, robot core components, and AI-native smart factory solutions, demonstrating end-to-end capabilities spanning complete robot systems, critical parts, industrial software, and smart factory architecture.

“The true value of embodied intelligence lies in understanding real industrial tasks: combining the strength, precision, and stability of machines with human experience and judgment to drive a genuine paradigm of ‘machine-assisted, human-machine collaboration,'” said Wang Chunlei, deputy general manager of the Robotics Business Unit at Shanghai Electric Automation Group.

Shanghai Electric’s robotics portfolio covers five key industrial scenarios: connector insertion, electrical operations, flexible sorting, intelligent assembly, and pipe processing. Highlights include:

“SUYUAN” bipedal humanoid robot: With 41 degrees of freedom for enhanced mobility, it is equipped with a multimodal visual sensing system on the head and torso, along with a dual-battery hot-swap system. It is well-suited for inspection, material handling, and assembly tasks.”TUOYUAN” industrial wheeled humanoid robot: Powered by an embodied intelligence foundation model and force-position hybrid control, it is capable of multi-spec connector insertion, material sorting, and loading/unloading of automotive sheet metal parts.”Mermaid” bionic wheeled humanoid robot: Capable of autonomously identifying buttons, knobs, and air switches, it generates real-time operation paths.Autonomous pipe inner-wall chamfering robot: Designed for confined spaces, it can position and process thousands of hole edges with accuracy within 1 millimeter while transmitting data in real time.

Shanghai Electric also showcased its portfolio of core components ranging from power-output to end effectors. Among them, the planetary roller screw offers more than three times the load capacity of traditional ball screws, while the DexHand dexterous hand is designed to meet diverse gripping and manipulation requirements.

Shanghai Electric launched 51 AI models and agents under its “StarCloud Intelligent Manufacturing” series across three domains: R&D and design, production and manufacturing, and operations and maintenance—covering critical equipment processes such as process optimization and wind power facility maintenance.

These industrial agents are embedded in robotic decision-making systems and the operational logic of AI-native smart factories, transforming industrial expertise into digitized, reusable capabilities. They support production-line scheduling, quality inspection, and predictive maintenance, driving the evolution of manufacturing systems from experience-driven to data-driven operations.

Shanghai Electric also released the “AI-Native Smart Factory Technology White Paper,” proposing an active evolution architecture that enables real‑time, closed‑loop optimization of production data, giving the factory self‑perception, self‑decision, and self‑execution capabilities. Built on First Principles, the AI‑native smart factory vertically integrates process flows, industrial software, agents, and smart equipment to dismantle traditional hierarchies while horizontally bridging data silos. The architecture features three core layers: the AI factory brain as the “control center,” industrial agents and embodied robots as the “execution network,” and the physical twin as the “digital mirror.”

Leveraging its deep industrial expertise and comprehensive solution capabilities, Shanghai Electric will continue to drive the implementation of AI in industrial settings, tackle technical challenges facing embodied intelligence in complex scenarios, accelerate the large‑scale deployment of AI‑native smart factories, and deliver replicable solutions across diverse manufacturing environments.

SOURCE Shanghai Electric

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