A land requires use analysis to obtain the best increase in the land value. Likewise with the land of an urban traditional market. Highest and Best Use (HBU) analysis is needed to get the best alternative property on the land. A traditional market is one place where the activity of trading centre. However, along with the development of the era of the existence of traditional markets increasingly abandoned. One of them is Pucang Anom market located in Pucang Anom Street, Surabaya. Market conditions on the second floor are quiet and lack of interest. In addition, the market conditions are old and look shabby. Therefore, it is necessary to revive Pucang Anom market so that it can produce the highest land value. Alternative land use is analysed using HBU method. The analysis began with an explorative study to explore alternatives of land use, afterwards it continued with four stages analysis including regulatory compliance and permits, possibility and affordability of construction, financial feasibility, and maximum productivity of land. The results of this study found an alternative mixed use between market and apartment as the best options with the highest land value and maximum productivity is 210%.

Purpose This paper aims to investigate the effect of business incubation metrics based on an adaptation of the lean start-up (LS) framework on start-up survival after incubation. This study also analyzes the obstacles in implementing the LS framework as incubation metrics. Design/methodology/approach This study uses mixed methods. Quantitative research using multiple linear regression was applied to the data of 30 start-ups incubated at Bandung Techno Park for the 2014–2017 period and survival tracking data after the incubation. A qualitative approach to complete the explanatory work was conducted through in-depth interviews with 12 respondents, including start-up graduates from the incubation program, program managers and mentors. Findings This study confirms that several LS incubation metrics significantly affect start-up sustainability after incubation. In addition, this study also explains several problems in applying the LS discipline that needs attention to increase incubation success. Research limitations/implications Research was conducted only at one technology business incubator (TBI) model that focuses on digital start-ups in the emerging ecosystem. Research results can be biased in different situations and ecosystems. Practical implications The explanation of the relationship of LS-based incubation metrics to the survival of start-ups, as well as the challenges of their implementation, can be a reference for TBI management to consider and prioritize intervention strategies, thereby improving TBI’s business processes and increasing the success rate of incubated start-ups. Social implications The creation of university start-ups and spin-offs has become a key performance indicator mandatory for technology universities in Indonesia. The existence of TBI institutions in universities as channels of technology commercialization is essential. The incubator’s success in creating a new technology-based company will have a significant social impact on the surrounding environment. Originality/value Although the LS method is popular in start-up communities and among practitioners, it is rarely used in the incubation process at universities. These results can be considered for university TBIs to explore LS as an incubation management tool to increase the success rate of incubated start-ups.

This study aims To measure the level of technology readiness level (TRL) and to assess user acceptance to use the transportation management system software utilizing technology acceptance model (TAM) approach. Technology acceptance model (TAM) is used to describe the three components, i.e., perceived usefulness, perceived ease of use, and behavioral intention to use . To ensure that these 3 components are important factors of TAM, authors utilized factor analysis method, whereas t-test calculations were carried out to find out the relationships between components. The results show that the TRL of transportation management system software readiness is 9 which means that it is ready to be used widely in the company. The factor analysis shows that TAM is influenced by the three components, which is able to explain about 66.8% of variations. In the meantime the t test shows that all hypotheses are accepted, so there are significant relationships between components analyzed. To summarize, the authors conclude that the company can benefit the usefulness and ease of use of the transportation management system software developed, so that the behavioral intention to use and the utilization of this technological innovation can be optimized.

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