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The purpose of this paper is to standardised four-step flexibility assessment methodology for evaluating the available electrical load reduction or increase a building can provide in response to a signal from an aggregator or grid operator.

The four steps in the methodology consist of Step 1: systems, loads, storage and generation identification; Step 2: flexibility characterisation; Step 3: scenario modelling; and Step 4: key performance indicator (KPI) label.

A detailed case study for one building, validated through on-site experiments, verified the feasibility and accuracy of the approach.

The results were benchmarked against available demonstration studies but could benefit from the future development of standardised benchmarks.

The ease of implementation enables building operators to quickly and cost effectively evaluate the flexibility of their building. By clearly defining the flexibility range, the KPI label enables contract negotiation between stakeholders for demand side services. It may also be applicable as a smart readiness indicator.

The novel KPI label has the capability to operationalise the concept of building flexibility to a wider spectrum of society, enabling smart grid demand response roll-out to residential and small commercial customers.

This paper fulfils an identified need for an early stage flexibility assessment which explicitly includes source selection that can be implemented in an offline manner without the need for extensive real-time data acquisition, ICT platforms or additional metre and sensor installations.

This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region.

A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter.

All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain.

This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill.

The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

The purpose of this research is to develop a scale for measuring customer value co-creation attitude (CVCCA).

Scale development procedures are used for item generation, item purification and validation. Two studies are conducted. In Study 1, exploratory and confirmatory factor analysis is used to generate and confirm the factorial structure of the CVCCA construct. Study 2 validates the scale on a large field sample.

The study develops a new scale for measuring CVCCA. Results suggest that CVCCA is a higher-order construct comprising three dimensions: interaction attitude, knowledge sharing attitude and responsive attitude. Additionally, experiential value significantly predicts CVCCA, which subsequently leads to customer value co-creation behaviour confirming nomological validity of the scale.

The CVCCA scale should be of interest for researchers in exploring factors and outcomes of CVCCA. The scale is useful to managers who are interested in measuring their customers’ co-creation of value attitude and their willingness to engage in value co-creation behaviour.

This is the first scale using the service logic of marketing lens. The scale is found to be a valid and reliable tool to measure customer attitude to engage in value co-creation.

Studies showed that a proactive delivery system employing innovative artificial intelligence (AI) in urban services might perform better. This has become an important national policy for many countries. Thus, this study aims to explore the influence of AI in urban services in Malaysia.

Official documents such as Structure Plan and Government Transformation Programme Policy Document covering various levels of cities in Malaysia, articles related to urban studies mostly written by researchers regarding urban growth in Malaysia and the Urban Development Bulletin from the Federal Department of Town and Country Planning from 1957 to date were reviewed and analysed.

The findings show that the influence of AI in urban services has long existed and been carefully planned by local authorities since colonial times. The development of global digital technology influences the upgrading of AI in urban services in Malaysia. Also, the success of AI in these municipal services is influenced by the rate of information technology literacy among the urban population. These developments have led to the definition of a conceptual city.

This paper's findings and conclusion were based on reviewed literature but did not compromise the strength of this paper. Thus, as part of the implications for future research, mixed-methods research design has been suggested.

As part of the implications, this article intends to promote AI in urban services in Malaysia and other developing countries with similar urban services challenges.

This paper encourages AI applications in urban services because it enhances service delivery performance. This intends to key stakeholders to promote AI via policies across Malaysia's urban services as part of the study implications.